jeanfranc0/nips2017

A list of resources for all invited talks, tutorials, workshops and presentations at NIPS 2017

NIPS 2017

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!

Check out Deep Hunt - a curated monthly AI newsletter for this repo as a blog post and follow me on Twitter.

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

• On Bayesian Deep Learning and Deep Bayesian Learning

  Yee Whye Teh

  Video

• AlphaZero - Mastering Games without human knowledge

  David Silver

  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

WiML

• Bayesian machine learning: Quantifying uncertainty and robustness at scale

  Tamara​ ​Broderick​

  Slides · Video · Code

• Towards Communication-Centric Multi-Agent Deep Reinforcement Learning for Guarding a Territory

  Aishwarya​ ​Unnikrishnan

  Slides · Video · Code

• Graph convolutional networks can encode three-dimensional genome architecture in deep learning models for genomics

  Peyton​ ​Greenside​

  Slides · Video · Code

• Machine Learning for Social Science

  Hannah​ ​Wallach​

  Slides · Video · Code

• Fairness Aware Recommendations

  Palak​ ​Agarwal​

  Slides · Video · Code

• Reinforcement Learning with a Corrupted Reward Channel

  Victoria​ ​Krakivna​

  Slides · Video · Code

• Improving health-care: challenges and opportunities for reinforcement learning

  Joelle​ ​Pineau​

  Slides · Video · Code

• Harnessing Adversarial Attacks on Deep Reinforement Learning for Improving Robustness

  Zhenyi​ ​Tang​

  Slides · Video · Code

• Time-Critical Machine Learning

  Nina​ ​Mishra​

  Slides · Video · Code

• A General Framework for Evaluating Callout Mechanisms in Repeated Auctions

  Hoda​ ​Heidari​

  Slides · Video · Code

• Engaging Experts: A Dirichlet Process Approach to Divergent Elicited Priors in Social Science

  Sarah​ ​Bouchat​

  Slides · Video · Code

• Representation Learning in Large Attributed Graphs

  Nesreen​ ​K​ ​Ahmed​

  Slides · Video · Code