Repository: kihosuh/nips_2017 Branch: master Commit: 39a534087ec9 Files: 1 Total size: 10.8 KB Directory structure: gitextract_f3tkdsfa/ └── README.md ================================================ FILE CONTENTS ================================================ ================================================ FILE: README.md ================================================ # NIPS 2017 Accumulation of sources from NIPS 2017 in Long Beach, CA. Check out more about NIPS on https://nips.cc/ Currently collecting and feel free to pull requests, make issues or give feedbacks! ## Table of Contents - [Tutorials](#tutorials) - [Invited Talks](#invited-talks) - [Symposiums and Workshops](#symposiums-and-workshops) - [Orals and Spotlights](#orals-and-spotlights) - [Blogs and Podcasts](#blogs-and-podcasts) ## Tutorials - **Deep Learning: Practice and Trends** by Nando de Freitas, Scott Reed, Oriol Vinyals [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1552060484885185/) · [[Youtube]](https://www.youtube.com/watch?v=YJnddoa8sHk) · [[Slides]](https://docs.google.com/presentation/d/e/2PACX-1vQMZsWfjjLLz_wi8iaMxHKawuTkdqeA3Gw00wy5dBHLhAkuLEvhB7k-4LcO5RQEVFzZXfS6ByABaRr4/pub?slide=id.g2b178fe261_0_1280) - **Reinforcement Learning with People** by Emma Brunskill [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1555771847847382/) · Youtube · Slides - **A Primer on Optimal Transport** by Marco Cuturi, Justin M Solomon Facebook_Video · Youtube · Slides - **Deep Probabilistic Modelling with Gaussian Processes** by Neil D Lawrence [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1552223308202236/) · Youtube · [[Slides]](http://inverseprobability.com/talks/lawrence-nips17/deep-probabilistic-modelling-with-gaussian-processes.html) - **Fairness in Machine Learning** by Solon Barocas, Moritz Hardt Facebook_Video · Youtube · [[Slides]](http://mrtz.org/nips17/#/) - **Statistical Relational Artificial Intelligence: Logic, Probability and Computation** by Luc De Raedt, David Poole, Kristian Kersting, Sriraam Natarajan [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1552222671535633/) · Youtube · Slides - **Engineering and Reverse-Engineering Intelligence Using Probabilistic Programs, Program Induction, and Deep Learning** by Josh Tenenbaum, Vikash K Mansinghka [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1552446408179926/) · Youtube · Slides - **Differentially Private Machine Learning: Theory, Algorithms and Applications** by Kamalika Chaudhuri, Anand D Sarwate Facebook_Video · Youtube · Slides - **Geometric Deep Learning on Graphs and Manifolds** by Michael Bronstein, Joan Bruna, arthur szlam, Xavier Bresson, Yann LeCun Facebook_Video · [[Youtube]](https://www.youtube.com/watch?v=LvmjbXZyoP0) · Slides This website is a treasure box for geometric deep learning. Check out http://geometricdeeplearning.com/ ## Invited Talks - **Opening Remarks / Powering the next 100 years** by Terrence Sejnowki / John Platt [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1552610871496813/) · Youtube · Slides - **Why AI Will Make it Possible to Reprogram the Human Genome** by Brendan J Frey [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1553236368100930/) · [[Youtube]](https://www.youtube.com/watch?v=QJLQBSQJEus) · Slides - **Random Features for Large-Scale Kernel Machines** by Ali Rahimi, Benjamin Recht (Test of Time Award) Facebook_Video · [[Youtube]](https://www.youtube.com/watch?v=Qi1Yry33TQE) · Slides · [[Related Blog by inFERENCe]](http://www.inference.vc/my-thoughts-on-alchemy/) - **The Trouble with Bias** by Kate Crawford Facebook_Video · [[Youtube]](https://www.youtube.com/watch?v=6Uao14eIyGc) · Slides - **The Unreasonable Effectiveness of Structure** by Lise Getoor [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1554329184658315/) · [[Youtube]](https://www.youtube.com/watch?v=t4k5LKCpboc) · Slides - **Deep Learning for Robotics** by Pieter Abbeel [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1554594181298482/) · Youtube · [[Slides]](https://www.dropbox.com/s/fdw7q8mx3x4wr0c/2017_12_xx_NIPS-keynote-final.pdf?dl=0) - **Learning State Representations** by Yael Niv [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1555427447881822/) · Youtube · Slides - **On Bayesian Deep Learning and Deep Bayesian Learning** by Yee Whye Teh [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1555493854541848/) · [[Youtube]](https://www.youtube.com/watch?v=YJnddoa8sHk) · Slides ## Symposiums and Workshops - **AlphaZero - Mastering Games without human knowledge** by David Silver Facebook_Video · [[Youtube]](https://www.youtube.com/watch?v=A3ekFcZ3KNw) · Slides - **GANs for Creativity and Design** by Ian Goodfellow Facebook_Video · Youtube · [[Slides]](http://www.iangoodfellow.com/slides/2017-12-08-creativity.pdf) - **GANs for Limited Labeled Data** by Ian Goodfellow Facebook_Video · Youtube · [[Slides]](http://www.iangoodfellow.com/slides/2017-12-09-label.pdf) - **Machine Learning for Systems and Systems for Machine Learning** by Jeff Dean Facebook_Video · Youtube · [[Slides]](http://learningsys.org/nips17/assets/slides/dean-nips17.pdf) - **NSML: A Machine Learning Platform That Enables You to Focus on Your Models** by Nako Sung Facebook_Video · [[Youtube]](https://www.youtube.com/watch?v=3Qub0wL9Gwc) · Slides - **Teaching Artificial Intelligence to Run (NIPS 2017)** by CrowdAI Facebook_Video · [[Youtube]](https://www.youtube.com/watch?v=rhNxt0VccsE) · Slides ## Orals and Spotlights - **Algorithm (Tuesday 10:40~12:00)** [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1553335844757649/) (Diffusion Approximations for Online Principal Component Estimation and Global Convergence, Positive-Unlabeled Learning with Non- Negative Risk Estimator, An Applied Algorithmic Foundation for Hierarchical Clustering, Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results, QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding, Inhomogeneous Hypergraph Clustering with Applications, K-Medoids for K-Means Seeding, Online Learning with Transductive Regret, Matrix Norm Estimation from a Few Entries, Semisupervised Clustering, AND-Queries and Locally Encodable Source Coding) - **Optimization (Tuesday 10:40~12:00)** [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1553355798088987/) (On the Optimization Landscape of Tensor Decompositions, Robust Optimization for Non-Convex Objectives, Bayesian Optimization with Gradients, Gradient Descent Can Take Exponential Time to Escape Saddle Points, Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration, Limitations on Variance-Reduction and Acceleration Schemes for Finite Sums Optimization, Implicit Regularization in Matrix Factorization, Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls, Acceleration and Averaging in Stochastic Descent Dynamics, When Cyclic Coordinate Descent Beats Randomized Coordinate Descent) - **Theory (Tuesday 14:50~15:50)** [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1553355798088987/) (Safe and Nested Subgame Solving for Imperfect-Information Games, A graph-theoretic approach to multitasking, Information-theoretic analysis of generalization capability of learning algorithms, Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee, Clustering Billions of Reads for DNA Data Storage, On the Complexity of Learning Neural Networks, Multiplicative Weights Update with Constant Step-Size in Congestion Games: Convergence, Limit Cycles and Chaos, Estimating Mutual Information for Discrete-Continuous Mixtures) - **Algorithms, Optimization (Tuesday 14:50~15:50)** [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1553537531404147/) (Streaming Weak Submodularity: Interpreting Neural Networks on the Fly, A Unified Approach to Interpreting Model Predictions, Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays, Generalized Linear Model Regression under Distance-to-set Penalties, Decomposable Submodular Function Minimization: Discrete and Continuous, Unbiased estimates for linear regression via volume sampling, On Frank-Wolfe and Equilibrium Computation, On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models) - **Deep Learning, Applications (Tuesday 16:20~18:00)** [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1553634558061111/) (Unsupervised object learning from dense equivariant image labelling, Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts, Eigen-Distortions of Hierarchical Representations, Towards Accurate Binary Convolutional Neural Network, Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model, Poincaré Embeddings for Learning Hierarchical Representations, Deep Hyperspherical Learning, What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?, One-Sided Unsupervised Domain Mapping, Deep Mean-Shift Priors for Image Restoration, Deep Voice 2: Multi-Speaker Neural Text-to-Speech, Graph Matching via Multiplicative Update Algorithm, Dynamic Routing Between Capsules, Modulating early visual processing by language) - **Algorithms (Tuesday 16:20~18:00)** [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1553635538061013/) (A Linear-Time Kernel Goodness-of-Fit Test, Generalization Properties of Learning with Random Features, Communication-Efficient Distributed Learning of Discrete Distributions, Optimistic posterior sampling for reinforcement learning: worst-case regret bounds, Regret Analysis for Continuous Dueling Bandit, Minimal Exploration in Structured Stochastic Bandits, Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe, Diving into the shallows: a computational perspective on large-scale shallow learning, Monte-Carlo Tree Search by Best Arm Identification, A framework for Multi-A(rmed)/B(andit) Testing with Online FDR Control, Parameter-Free Online Learning via Model Selection, Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction, Gaussian Quadrature for Kernel Features, Learning Linear Dynamical Systems via Spectral Filtering) - **Videos of papers recorded before the conference** [[Video]](https://nips.cc/Conferences/2017/Videos) ## Blogs and Podcasts - **NIPS 2017 — notes and thoughs** by Olgalitech https://olgalitech.wordpress.com/2017/12/12/nips-2017-notes-and-thoughs/ - **NIPS 2017 Notes** by David Abel https://cs.brown.edu/~dabel/blog/posts/misc/nips_2017.pdf - **데이터지능 팟캐스트 E6: Deep learning in NIPS2017** hosted by Jin Young Kim and Terry Um (in Korean) https://www.youtube.com/watch?v=Vm0gQ2eUtBs - **NIPS 2017 Summary! (unless an "official" one gets posted, and then remove this dreck)** https://www.reddit.com/r/MachineLearning/comments/7j2v74/d_nips_2017_summary_unless_an_official_one_gets/
gitextract_f3tkdsfa/ └── README.md
Condensed preview — 1 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (11K chars).
[
{
"path": "README.md",
"chars": 11023,
"preview": "# NIPS 2017\nAccumulation of sources from NIPS 2017 in Long Beach, CA. Check out more about NIPS on https://nips.cc/\n\nCur"
}
]
About this extraction
This page contains the full source code of the kihosuh/nips_2017 GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 1 files (10.8 KB), approximately 2.9k tokens. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.