[
  {
    "path": "README.md",
    "content": "# NIPS 2017\nAccumulation of sources from NIPS 2017 in Long Beach, CA. Check out more about NIPS on https://nips.cc/\n\nCurrently collecting and feel free to pull requests, make issues or give feedbacks!\n\n## Table of Contents\n- [Tutorials](#tutorials)\n- [Invited Talks](#invited-talks)\n- [Symposiums and Workshops](#symposiums-and-workshops)\n- [Orals and Spotlights](#orals-and-spotlights)\n- [Blogs and Podcasts](#blogs-and-podcasts)\n\n\n## Tutorials\n\n- **Deep Learning: Practice and Trends** by Nando de Freitas, Scott Reed, Oriol Vinyals\n  \n  [[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)\n\n- **Reinforcement Learning with People** by Emma Brunskill\n  \n  [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1555771847847382/) · Youtube · Slides\n\n- **A Primer on Optimal Transport** by Marco Cuturi, Justin M Solomon\n  \n  Facebook_Video · Youtube · Slides\n\n- **Deep Probabilistic Modelling with Gaussian Processes** by Neil D Lawrence\n  \n  [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1552223308202236/) · Youtube · [[Slides]](http://inverseprobability.com/talks/lawrence-nips17/deep-probabilistic-modelling-with-gaussian-processes.html)\n\n- **Fairness in Machine Learning** by Solon Barocas, Moritz Hardt\n  \n  Facebook_Video · Youtube · [[Slides]](http://mrtz.org/nips17/#/)\n\n- **Statistical Relational Artificial Intelligence: Logic, Probability and Computation** by Luc De Raedt, David Poole, Kristian Kersting, Sriraam Natarajan\n  \n  [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1552222671535633/) · Youtube · Slides\n\n- **Engineering and Reverse-Engineering Intelligence Using Probabilistic Programs, Program Induction, and Deep Learning** by Josh Tenenbaum, Vikash K Mansinghka\n  \n  [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1552446408179926/) · Youtube · Slides\n\n- **Differentially Private Machine Learning: Theory, Algorithms and Applications** by Kamalika Chaudhuri, Anand D Sarwate\n  \n  Facebook_Video · Youtube · Slides\n\n- **Geometric Deep Learning on Graphs and Manifolds** by Michael Bronstein, Joan Bruna, arthur szlam, Xavier Bresson, Yann LeCun\n  \n  Facebook_Video · [[Youtube]](https://www.youtube.com/watch?v=LvmjbXZyoP0) · Slides\n  \n  This website is a treasure box for geometric deep learning. Check out http://geometricdeeplearning.com/\n\n\n## Invited Talks\n\n- **Opening Remarks / Powering the next 100 years** by Terrence Sejnowki / John Platt\n  \n  [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1552610871496813/) · Youtube · Slides\n\n- **Why AI Will Make it Possible to Reprogram the Human Genome** by Brendan J Frey\n\n  [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1553236368100930/) · [[Youtube]](https://www.youtube.com/watch?v=QJLQBSQJEus) · Slides\n\n- **Random Features for Large-Scale Kernel Machines** by Ali Rahimi, Benjamin Recht (Test of Time Award)\n  \n  Facebook_Video · [[Youtube]](https://www.youtube.com/watch?v=Qi1Yry33TQE) · Slides · [[Related Blog by inFERENCe]](http://www.inference.vc/my-thoughts-on-alchemy/)\n\n- **The Trouble with Bias** by Kate Crawford\n  \n  Facebook_Video · [[Youtube]](https://www.youtube.com/watch?v=6Uao14eIyGc) · Slides\n\n- **The Unreasonable Effectiveness of Structure** by Lise Getoor\n  \n  [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1554329184658315/) · [[Youtube]](https://www.youtube.com/watch?v=t4k5LKCpboc) · Slides\n\n- **Deep Learning for Robotics** by Pieter Abbeel\n  \n  [[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)\n\n- **Learning State Representations** by Yael Niv\n  \n  [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1555427447881822/) · Youtube · Slides\n\n- **On Bayesian Deep Learning and Deep Bayesian Learning** by Yee Whye Teh\n  \n  [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1555493854541848/) · [[Youtube]](https://www.youtube.com/watch?v=YJnddoa8sHk) · Slides\n\n\n## Symposiums and Workshops \n\n- **AlphaZero - Mastering Games without human knowledge** by David Silver\n  \n  Facebook_Video · [[Youtube]](https://www.youtube.com/watch?v=A3ekFcZ3KNw) · Slides\n\n- **GANs for Creativity and Design** by Ian Goodfellow\n\n  Facebook_Video · Youtube · [[Slides]](http://www.iangoodfellow.com/slides/2017-12-08-creativity.pdf)\n\n- **GANs for Limited Labeled Data** by Ian Goodfellow\n\n  Facebook_Video · Youtube · [[Slides]](http://www.iangoodfellow.com/slides/2017-12-09-label.pdf)\n  \n- **Machine Learning for Systems and Systems for Machine Learning** by Jeff Dean\n  \n  Facebook_Video · Youtube · [[Slides]](http://learningsys.org/nips17/assets/slides/dean-nips17.pdf)\n  \n- **NSML: A Machine Learning Platform That Enables You to Focus on Your Models** by Nako Sung\n\n  Facebook_Video · [[Youtube]](https://www.youtube.com/watch?v=3Qub0wL9Gwc) · Slides\n  \n- **Teaching Artificial Intelligence to Run (NIPS 2017)** by CrowdAI\n\n  Facebook_Video · [[Youtube]](https://www.youtube.com/watch?v=rhNxt0VccsE) · Slides\n\n\n## Orals and Spotlights\n- **Algorithm (Tuesday 10:40~12:00)** \n\n  [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1553335844757649/)\n\n  (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) \n\n- **Optimization (Tuesday 10:40~12:00)**\n\n  [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1553355798088987/)\n  \n  (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)\n\n- **Theory (Tuesday 14:50~15:50)**\n\n  [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1553355798088987/)\n  \n  (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)\n  \n- **Algorithms, Optimization (Tuesday 14:50~15:50)**\n\n  [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1553537531404147/)\n  \n  (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)\n  \n- **Deep Learning, Applications (Tuesday 16:20~18:00)**\n\n  [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1553634558061111/)\n  \n  (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)\n\n- **Algorithms (Tuesday 16:20~18:00)**\n\n  [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1553635538061013/)\n  \n  (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)\n\n- **Videos of papers recorded before the conference** \n  \n  [[Video]](https://nips.cc/Conferences/2017/Videos)\n\n## Blogs and Podcasts\n- **NIPS 2017 — notes and thoughs** by Olgalitech https://olgalitech.wordpress.com/2017/12/12/nips-2017-notes-and-thoughs/\n\n- **NIPS 2017 Notes** by David Abel https://cs.brown.edu/~dabel/blog/posts/misc/nips_2017.pdf\n\n- **데이터지능 팟캐스트 E6: Deep learning in NIPS2017** hosted by Jin Young Kim and Terry Um (in Korean) https://www.youtube.com/watch?v=Vm0gQ2eUtBs\n\n- **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/\n"
  }
]