NIPS 2017

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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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Contents

• Invited Talks

• Tutorials

• Workshops

• WiML

Invited Talks

• Powering the next 100 years

  John Platt

  Slides · Video · Code

• Why AI Will Make it Possible to Reprogram the Human Genome

  Brendan J Frey

  Video

• The Trouble with Bias

  Kate Crawford

  Video

• The Unreasonable Effectiveness of Structure

  Lise Getoor

  Slides · Video

• Deep Learning for Robotics

  Pieter Abbeel

  Slides · Video · Code

• Learning State Representations

  Yael Niv

  Video

• On Bayesian Deep Learning and Deep Bayesian Learning

  Yee Whye Teh

  Video

Tutorials

• Deep Learning: Practice and Trends

  Nando de Freitas · Scott Reed · Oriol Vinyals

  Slides · Video · Code

• Reinforcement Learning with People

  Emma Brunskill

  Slides · Video · Code

• A Primer on Optimal Transport

  Marco Cuturi · Justin M Solomon

  Slides · Video · Code

• Deep Probabilistic Modelling with Gaussian Processes

  Neil D Lawrence

  Slides · Video · Code

• Fairness in Machine Learning

  Solon Barocas · Moritz Hardt

  Slides · Video · Code

• Statistical Relational Artificial Intelligence: Logic, Probability and Computation

  Luc De Raedt · David Poole · Kristian Kersting · Sriraam Natarajan

  Slides · Video · Code

• Engineering and Reverse-Engineering Intelligence Using Probabilistic Programs, Program Induction, and Deep Learning

  Josh Tenenbaum · Vikash K Mansinghka

  Slides · Video · Code

• Differentially Private Machine Learning: Theory, Algorithms and Applications

  Kamalika Chaudhuri · Anand D Sarwate

  Slides · Video · Code

• Geometric Deep Learning on Graphs and Manifolds

  Michael Bronstein · Joan Bruna · arthur szlam · Xavier Bresson · Yann LeCun

  Slides · Video · Code ​

Workshops

• ML Systems Workshop @ NIPS 2017

  Aparna Lakshmiratan · Sarah Bird · Siddhartha Sen · Christopher Ré · Li Erran Li · Joseph Gonzalez · Daniel Crankshaw

  • A distributed execution engine for emerging AI applications

    Ion Stoica

  • The Case for Learning Database Indexes

  • Federated Multi-Task Learning

    Virginia Smith

  • Accelerating Persistent Neural Networks at Datacenter Scale

    Daniel Lo

  • DLVM: A modern compiler framework for neural network DSLs

    Richard Wei · Lane Schwartz · Vikram Adve

  • Machine Learning for Systems and Systems for Machine Learning

    Jeff Dean

  • Creating an Open and Flexible ecosystem for AI models with ONNX

    Sarah Bird · Dmytro Dzhulgakov

  • NSML: A Machine Learning Platform That Enables You to Focus on Your Models

    Nako Sung

  • DAWNBench: An End-to-End Deep Learning Benchmark and Competition

    Cody Coleman

• Bayesian Deep Learning

  Yarin Gal · José Miguel Hernández-Lobato · Christos Louizos · Andrew G Wilson · Diederik P. (Durk) Kingma · Zoubin Ghahramani · Kevin P Murphy · Max Welling

  • Why Aren't You Using Probabilistic Programming?

    Dustin Tran

  • Automatic Model Selection in BNNs with Horseshoe Priors

    Finale Doshi

  • Deep Bayes for Distributed Learning, Uncertainty Quantification and Compression

    Max Welling

  • Stochastic Gradient Descent as Approximate Bayesian Inference

    Matt Hoffman

  • Recent Advances in Autoregressive Generative Models

    Nal Kalchbrenner

  • Deep Kernel Learning

    Russ Salakhutdinov

  • Bayes by Backprop

    Meire Fortunato

  • How do the Deep Learning layers converge to the Information Bottleneck limit by Stochastic Gradient Descent?

    Naftali (Tali) Tishby

• Learning with Limited Labeled Data: Weak Supervision and Beyond

  Isabelle Augenstein · Stephen Bach · Eugene Belilovsky · Matthew Blaschko · Christoph Lampert · Edouard Oyallon · Emmanouil Antonios Platanios · Alexander Ratner · Christopher Ré

  • Welcome Note

  • Tales from fMRI: Learning from limited labeled data

    Gaël Varoquaux

  • Learning from Limited Labeled Data (But a Lot of Unlabeled Data)

    Tom Mitchell

  • Light Supervision of Structured Prediction Energy Networks

    Andrew McCallum

  • Forcing Neural Link Predictors to Play by the Rules

    Sebastian Riedel

  • Panel: Limited Labeled Data in Medical Imaging

    Daniel Rubin · Matt Lungren · Ina Fiterau

  • Sample and Computationally Efficient Active Learning Algorithms

    Nina Balcan

  • That Doesn't Make Sense! A Case Study in Actively Annotating Model Explanations

    Sameer Singh

  • Overcoming Limited Data with GANs

    Ian Goodfellow

  • What’s so Hard About Natural Language Understanding?

    Alan Ritter

  • Closing Remarks

• Advances in Approximate Bayesian Inference

  Francisco Ruiz · Stephan Mandt · Cheng Zhang · James McInerney · Dustin Tran · Tamara Broderick · Michalis Titsias · David Blei · Max Welling

  • Learning priors, likelihoods, or posteriors

    Iain Murray

  • Learning Implicit Generative Models Using Differentiable Graph Tests

    Josip Djolonga

  • Gradient Estimators for Implicit Models)

    Yingzhen Li

  • Variational Autoencoders for Recommendation

    Dawen Liang

  • Approximate Inference in Industry: Two Applications at Amazon

    Cedric Archambeau

  • Variational Inference based on Robust Divergences

    Futoshi Futami

  • Adversarial Sequential Monte Carlo

    Kira Kempinska

  • Scalable Logit Gaussian Process Classification

    Florian Wenzel

  • Variational inference in deep Gaussian processes

    Andreas Damianou

  • Taylor Residual Estimators via Automatic Differentiation

    Andrew Miller

  • Differential privacy and Bayesian learning

    Antti Honkela

  • Frequentist Consistency of Variational Bayes

    Yixin Wang

• Deep Learning at Supercomputer Scale

  Erich Elsen · Danijar Hafner · Zak Stone · Brennan Saeta

  • Generalization Gap

    Nitish Keskar

  • Closing the Generalization Gap

    Itay Hubara · Elad Hoffer

  • [Don’t Decay the Learning Rate, Increase the Batch