Videos and slides from NIPS 2017
GitHub - kihosuh/nips_2017: videos, slides, and others from NIPS 2017
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
Invited Talks
Symposiums and Workshops
Orals and Spotlights
Blogs and Podcasts

Tutorials


Deep Learning: Practice and Trends by Nando de Freitas, Scott Reed, Oriol Vinyals
[Facebook_Video] · [Youtube] · [Slides]


Reinforcement Learning with People by Emma Brunskill
[Facebook_Video] · 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] · Youtube · [Slides]


Fairness in Machine Learning by Solon Barocas, Moritz Hardt
Facebook_Video · Youtube · [Slides]


Statistical Relational Artificial Intelligence: Logic, Probability and Computation by Luc De Raedt, David Poole, Kristian Kersting, Sriraam Natarajan
[Facebook_Video] · Youtube · Slides


Engineering and Reverse-Engineering Intelligence Using Probabilistic Programs, Program Induction, and Deep Learning by Josh Tenenbaum, Vikash K Mansinghka
[Facebook_Video] · 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] · 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] · Youtube · Slides


Why AI Will Make it Possible to Reprogram the Human Genome by Brendan J Frey
[Facebook_Video] · [Youtube] · Slides


Random Features for Large-Scale Kernel Machines by Ali Rahimi, Benjamin Recht (Test of Time Award)
Facebook_Video · [Youtube] · Slides · [Related Blog by inFERENCe]


The Trouble with Bias by Kate Crawford
Facebook_Video · [Youtube] · Slides


The Unreasonable Effectiveness of Structure by Lise Getoor
[Facebook_Video] · [Youtube] · Slides


Deep Learning for Robotics by Pieter Abbeel
[Facebook_Video] · Youtube · [Slides]


Learning State Representations by Yael Niv
[Facebook_Video] · Youtube · Slides


On Bayesian Deep Learning and Deep Bayesian Learning by Yee Whye Teh
[Facebook_Video] · [Youtube] · Slides


Symposiums and Workshops


AlphaZero - Mastering Games without human knowledge by David Silver
Facebook_Video · [Youtube] · Slides


GANs for Creativity and Design by Ian Goodfellow
Facebook_Video · Youtube · [Slides]


GANs for Limited Labeled Data by Ian Goodfellow
Facebook_Video · Youtube · [Slides]


Machine Learning for Systems and Systems for Machine Learning by Jeff Dean
Facebook_Video · Youtube · [Slides]


NSML: A Machine Learning Platform That Enables You to Focus on Your Models by Nako Sung
Facebook_Video · [Youtube] · Slides


Teaching Artificial Intelligence to Run (NIPS 2017) by CrowdAI
Facebook_Video · [Youtube] · Slides


Orals and Spotlights


Algorithm (Tuesday 10:40~12:00)
[Facebook_Video]
(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]
(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]
(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]
(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]
(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]
(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]


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/


