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videos, slides, and others from NIPS 2017

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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

  • 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

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

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videos, slides, and others from NIPS 2017

deep-learningnipsmachine-learning