This year's Neural Information Processing Systems (NIPS) 2017 conference held at Long Beach Convention Center, Long Beach California has been the biggest ever! Here's a list of resources and slides of all invited talks, tutorials and workshops.
Contributions are welcome. You can add links via pull requests or create an issue to lemme know something I missed or to start a discussion. If you know the speakers, please ask them to upload slides online!
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Powering the next 100 years
John Platt
Slides · Video · Code
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Why AI Will Make it Possible to Reprogram the Human Genome
Brendan J Frey
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The Trouble with Bias
Kate Crawford
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The Unreasonable Effectiveness of Structure
Lise Getoor
Slides · Video
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Deep Learning for Robotics
Pieter Abbeel
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Learning State Representations
Yael Niv
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On Bayesian Deep Learning and Deep Bayesian Learning
Yee Whye Teh
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AlphaZero - Mastering Games without human knowledge
David Silver
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Deep Learning: Practice and Trends
Nando de Freitas · Scott Reed · Oriol Vinyals
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Reinforcement Learning with People
Emma Brunskill
Slides · Video · Code
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A Primer on Optimal Transport
Marco Cuturi · Justin M Solomon
Slides · Video · Code
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Deep Probabilistic Modelling with Gaussian Processes
Neil D Lawrence
Slides · Video · Code
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Fairness in Machine Learning
Solon Barocas · Moritz Hardt
Slides · Video · Code
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Statistical Relational Artificial Intelligence: Logic, Probability and Computation
Luc De Raedt · David Poole · Kristian Kersting · Sriraam Natarajan
Slides · Video · Code
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Engineering and Reverse-Engineering Intelligence Using Probabilistic Programs, Program Induction, and Deep Learning
Josh Tenenbaum · Vikash K Mansinghka
Slides · Video · Code
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Differentially Private Machine Learning: Theory, Algorithms and Applications
Kamalika Chaudhuri · Anand D Sarwate
Slides · Video · Code
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Geometric Deep Learning on Graphs and Manifolds
Michael Bronstein · Joan Bruna · arthur szlam · Xavier Bresson · Yann LeCun
Slides · Video · Code
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Aparna Lakshmiratan · Sarah Bird · Siddhartha Sen · Christopher Ré · Li Erran Li · Joseph Gonzalez · Daniel Crankshaw
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A distributed execution engine for emerging AI applications
Ion Stoica
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The Case for Learning Database Indexes
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Virginia Smith
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Accelerating Persistent Neural Networks at Datacenter Scale
Daniel Lo
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DLVM: A modern compiler framework for neural network DSLs
Richard Wei · Lane Schwartz · Vikram Adve
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Machine Learning for Systems and Systems for Machine Learning
Jeff Dean
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Creating an Open and Flexible ecosystem for AI models with ONNX
Sarah Bird · Dmytro Dzhulgakov
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NSML: A Machine Learning Platform That Enables You to Focus on Your Models
Nako Sung
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DAWNBench: An End-to-End Deep Learning Benchmark and Competition
Cody Coleman
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Yarin Gal · José Miguel Hernández-Lobato · Christos Louizos · Andrew G Wilson · Diederik P. (Durk) Kingma · Zoubin Ghahramani · Kevin P Murphy · Max Welling
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Why Aren't You Using Probabilistic Programming?
Dustin Tran
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Automatic Model Selection in BNNs with Horseshoe Priors
Finale Doshi
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Deep Bayes for Distributed Learning, Uncertainty Quantification and Compression
Max Welling
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Stochastic Gradient Descent as Approximate Bayesian Inference
Matt Hoffman
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Recent Advances in Autoregressive Generative Models
Nal Kalchbrenner
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Deep Kernel Learning
Russ Salakhutdinov
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Bayes by Backprop
Meire Fortunato
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How do the Deep Learning layers converge to the Information Bottleneck limit by Stochastic Gradient Descent?
Naftali (Tali) Tishby
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Isabelle Augenstein · Stephen Bach · Eugene Belilovsky · Matthew Blaschko · Christoph Lampert · Edouard Oyallon · Emmanouil Antonios Platanios · Alexander Ratner · Christopher Ré
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Tales from fMRI: Learning from limited labeled data
Gaël Varoquaux
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Learning from Limited Labeled Data (But a Lot of Unlabeled Data)
Tom Mitchell
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Light Supervision of Structured Prediction Energy Networks
Andrew McCallum
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Forcing Neural Link Predictors to Play by the Rules
Sebastian Riedel
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Panel: Limited Labeled Data in Medical Imaging
Daniel Rubin · Matt Lungren · Ina Fiterau
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Sample and Computationally Efficient Active Learning Algorithms
Nina Balcan
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That Doesn't Make Sense! A Case Study in Actively Annotating Model Explanations
Sameer Singh
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Overcoming Limited Data with GANs
Ian Goodfellow
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What’s so Hard About Natural Language Understanding?
Alan Ritter
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Bayesian machine learning: Quantifying uncertainty and robustness at scale
Tamara Broderick
Slides · Video · Code
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Towards Communication-Centric Multi-Agent Deep Reinforcement Learning for Guarding a Territory
Aishwarya Unnikrishnan
Slides · Video · Code
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Graph convolutional networks can encode three-dimensional genome architecture in deep learning models for genomics
Peyton Greenside
Slides · Video · Code
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Machine Learning for Social Science
Hannah Wallach
Slides · Video · Code
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Fairness Aware Recommendations
Palak Agarwal
Slides · Video · Code
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Reinforcement Learning with a Corrupted Reward Channel
Victoria Krakivna
Slides · Video · Code
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Improving health-care: challenges and opportunities for reinforcement learning
Joelle Pineau
Slides · Video · Code
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Harnessing Adversarial Attacks on Deep Reinforement Learning for Improving Robustness
Zhenyi Tang
Slides · Video · Code
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Time-Critical Machine Learning
Nina Mishra
Slides · Video · Code
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A General Framework for Evaluating Callout Mechanisms in Repeated Auctions
Hoda Heidari
Slides · Video · Code
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Engaging Experts: A Dirichlet Process Approach to Divergent Elicited Priors in Social Science
Sarah Bouchat
Slides · Video · Code
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Representation Learning in Large Attributed Graphs
Nesreen K Ahmed
Slides · Video · Code