└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # NIPS 2017 2 | Accumulation of sources from NIPS 2017 in Long Beach, CA. Check out more about NIPS on https://nips.cc/ 3 | 4 | Currently collecting and feel free to pull requests, make issues or give feedbacks! 5 | 6 | ## Table of Contents 7 | - [Tutorials](#tutorials) 8 | - [Invited Talks](#invited-talks) 9 | - [Symposiums and Workshops](#symposiums-and-workshops) 10 | - [Orals and Spotlights](#orals-and-spotlights) 11 | - [Blogs and Podcasts](#blogs-and-podcasts) 12 | 13 | 14 | ## Tutorials 15 | 16 | - **Deep Learning: Practice and Trends** by Nando de Freitas, Scott Reed, Oriol Vinyals 17 | 18 | [[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) 19 | 20 | - **Reinforcement Learning with People** by Emma Brunskill 21 | 22 | [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1555771847847382/) · Youtube · Slides 23 | 24 | - **A Primer on Optimal Transport** by Marco Cuturi, Justin M Solomon 25 | 26 | Facebook_Video · Youtube · Slides 27 | 28 | - **Deep Probabilistic Modelling with Gaussian Processes** by Neil D Lawrence 29 | 30 | [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1552223308202236/) · Youtube · [[Slides]](http://inverseprobability.com/talks/lawrence-nips17/deep-probabilistic-modelling-with-gaussian-processes.html) 31 | 32 | - **Fairness in Machine Learning** by Solon Barocas, Moritz Hardt 33 | 34 | Facebook_Video · Youtube · [[Slides]](http://mrtz.org/nips17/#/) 35 | 36 | - **Statistical Relational Artificial Intelligence: Logic, Probability and Computation** by Luc De Raedt, David Poole, Kristian Kersting, Sriraam Natarajan 37 | 38 | [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1552222671535633/) · Youtube · Slides 39 | 40 | - **Engineering and Reverse-Engineering Intelligence Using Probabilistic Programs, Program Induction, and Deep Learning** by Josh Tenenbaum, Vikash K Mansinghka 41 | 42 | [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1552446408179926/) · Youtube · Slides 43 | 44 | - **Differentially Private Machine Learning: Theory, Algorithms and Applications** by Kamalika Chaudhuri, Anand D Sarwate 45 | 46 | Facebook_Video · Youtube · Slides 47 | 48 | - **Geometric Deep Learning on Graphs and Manifolds** by Michael Bronstein, Joan Bruna, arthur szlam, Xavier Bresson, Yann LeCun 49 | 50 | Facebook_Video · [[Youtube]](https://www.youtube.com/watch?v=LvmjbXZyoP0) · Slides 51 | 52 | This website is a treasure box for geometric deep learning. Check out http://geometricdeeplearning.com/ 53 | 54 | 55 | ## Invited Talks 56 | 57 | - **Opening Remarks / Powering the next 100 years** by Terrence Sejnowki / John Platt 58 | 59 | [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1552610871496813/) · Youtube · Slides 60 | 61 | - **Why AI Will Make it Possible to Reprogram the Human Genome** by Brendan J Frey 62 | 63 | [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1553236368100930/) · [[Youtube]](https://www.youtube.com/watch?v=QJLQBSQJEus) · Slides 64 | 65 | - **Random Features for Large-Scale Kernel Machines** by Ali Rahimi, Benjamin Recht (Test of Time Award) 66 | 67 | Facebook_Video · [[Youtube]](https://www.youtube.com/watch?v=Qi1Yry33TQE) · Slides · [[Related Blog by inFERENCe]](http://www.inference.vc/my-thoughts-on-alchemy/) 68 | 69 | - **The Trouble with Bias** by Kate Crawford 70 | 71 | Facebook_Video · [[Youtube]](https://www.youtube.com/watch?v=6Uao14eIyGc) · Slides 72 | 73 | - **The Unreasonable Effectiveness of Structure** by Lise Getoor 74 | 75 | [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1554329184658315/) · [[Youtube]](https://www.youtube.com/watch?v=t4k5LKCpboc) · Slides 76 | 77 | - **Deep Learning for Robotics** by Pieter Abbeel 78 | 79 | [[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) 80 | 81 | - **Learning State Representations** by Yael Niv 82 | 83 | [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1555427447881822/) · Youtube · Slides 84 | 85 | - **On Bayesian Deep Learning and Deep Bayesian Learning** by Yee Whye Teh 86 | 87 | [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1555493854541848/) · [[Youtube]](https://www.youtube.com/watch?v=YJnddoa8sHk) · Slides 88 | 89 | 90 | ## Symposiums and Workshops 91 | 92 | - **AlphaZero - Mastering Games without human knowledge** by David Silver 93 | 94 | Facebook_Video · [[Youtube]](https://www.youtube.com/watch?v=A3ekFcZ3KNw) · Slides 95 | 96 | - **GANs for Creativity and Design** by Ian Goodfellow 97 | 98 | Facebook_Video · Youtube · [[Slides]](http://www.iangoodfellow.com/slides/2017-12-08-creativity.pdf) 99 | 100 | - **GANs for Limited Labeled Data** by Ian Goodfellow 101 | 102 | Facebook_Video · Youtube · [[Slides]](http://www.iangoodfellow.com/slides/2017-12-09-label.pdf) 103 | 104 | - **Machine Learning for Systems and Systems for Machine Learning** by Jeff Dean 105 | 106 | Facebook_Video · Youtube · [[Slides]](http://learningsys.org/nips17/assets/slides/dean-nips17.pdf) 107 | 108 | - **NSML: A Machine Learning Platform That Enables You to Focus on Your Models** by Nako Sung 109 | 110 | Facebook_Video · [[Youtube]](https://www.youtube.com/watch?v=3Qub0wL9Gwc) · Slides 111 | 112 | - **Teaching Artificial Intelligence to Run (NIPS 2017)** by CrowdAI 113 | 114 | Facebook_Video · [[Youtube]](https://www.youtube.com/watch?v=rhNxt0VccsE) · Slides 115 | 116 | 117 | ## Orals and Spotlights 118 | - **Algorithm (Tuesday 10:40~12:00)** 119 | 120 | [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1553335844757649/) 121 | 122 | (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) 123 | 124 | - **Optimization (Tuesday 10:40~12:00)** 125 | 126 | [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1553355798088987/) 127 | 128 | (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) 129 | 130 | - **Theory (Tuesday 14:50~15:50)** 131 | 132 | [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1553355798088987/) 133 | 134 | (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) 135 | 136 | - **Algorithms, Optimization (Tuesday 14:50~15:50)** 137 | 138 | [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1553537531404147/) 139 | 140 | (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) 141 | 142 | - **Deep Learning, Applications (Tuesday 16:20~18:00)** 143 | 144 | [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1553634558061111/) 145 | 146 | (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) 147 | 148 | - **Algorithms (Tuesday 16:20~18:00)** 149 | 150 | [[Facebook_Video]](https://www.facebook.com/nipsfoundation/videos/1553635538061013/) 151 | 152 | (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) 153 | 154 | - **Videos of papers recorded before the conference** 155 | 156 | [[Video]](https://nips.cc/Conferences/2017/Videos) 157 | 158 | ## Blogs and Podcasts 159 | - **NIPS 2017 — notes and thoughs** by Olgalitech https://olgalitech.wordpress.com/2017/12/12/nips-2017-notes-and-thoughs/ 160 | 161 | - **NIPS 2017 Notes** by David Abel https://cs.brown.edu/~dabel/blog/posts/misc/nips_2017.pdf 162 | 163 | - **데이터지능 팟캐스트 E6: Deep learning in NIPS2017** hosted by Jin Young Kim and Terry Um (in Korean) https://www.youtube.com/watch?v=Vm0gQ2eUtBs 164 | 165 | - **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/ 166 | --------------------------------------------------------------------------------