A list of resources for all invited talks, tutorials, workshops and presentations at NIPS 2016
A list of all invited talks, tutorials and presentations at Neural Information Processing Systems (NIPS) 2016 conference held at Barcelona and their resources
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Yann LeCun
Intelligent Biosphere
Drew Purves
Engineering Principles From Stable and Developing Brains
Saket Navlakha
Machine Learning and Likelihood-Free Inference in Particle Physics
Kyle Cranmer
Dynamic Legged Robots
Marc Raibert
Learning About the Brain: Neuroimaging and Beyond
Irina Rish
Reproducible Research: the Case of the Human Microbiome
Susan Holmes
Crowdsourcing: Beyond Label Generation
Jennifer Wortman Vaughan
Deep Reinforcement Learning Through Policy Optimization
Pieter Abbeel · John Schulman
Variational Inference: Foundations and Modern Methods
David Blei · Shakir Mohamed · Rajesh Ranganath
Theory and Algorithms for Forecasting Non-Stationary Time Series
Vitaly Kuznetsov · Mehryar Mohri
Nuts and Bolts of Building Applications using Deep Learning
Andrew Y Ng
Natural Language Processing for Computational Social Science
Cristian Danescu-Niculescu-Mizil · Lillian Lee
Generative Adversarial Networks
Ian Goodfellow
Large-Scale Optimization: Beyond Stochastic Gradient Descent and Convexity - Part I & Part II
Suvrit Sra · Francis Bach
ML Foundations and Methods for Precision Medicine and Healthcare
Suchi Saria · Peter Schulam
Designing Algorithms for Practical Machine Learning
Maya Gupta, Google Research.
On the Expressive Power of Deep Neural Networks
Maithra Raghu, Cornell Univ / Google Brain.
Sara Magliacane, VU Univ Amsterdam.
Towards a Reasoning Engine for Individualizing Healthcare
Suchi Saria, John Hopkins Univ.
Learning Representations from Time Series Data through Contextualized LSTMs
Madalina Fiterau, Stanford Univ.
Towards Conversational Recommender Systems
Konstantina Christakopoulou, Univ Minnesota.
Large-Scale Machine Learning through Spectral Methods: Theory & Practice
Anima Anandkumar, Amazon / UC Irvine.
Tamara Broderick, MIT and Sinead Williamson, UT Austin
Amy Zhang, Facebook.
Graphons and Machine Learning: Estimation of Sparse Massive Networks
Jennifer Chayes, Microsoft Research.
David Lopez-Paz · Leon Bottou · Alec Radford
Introduction to Generative Adversarial Networks
Ian Goodfellow
Soumith Chintala
Learning features to distinguish distributions
Arthur Gretton
Training Generative Neural Samplers using Variational Divergence
Sebastian Nowozin
Adversarially Learned Inference (ALI) and BiGANs
Aaron Courville
Energy-Based Adversarial Training and Video Prediction
Yann LeCun
David Silver · Satinder Singh · Pieter Abbeel · Xi Chen
Learning representations by stochastic gradient descent in cross-validation error
Rich Sutton
The Nuts and Bolts of Deep Reinforcement Learning Research
John Schulman
Raia Hadsell
Large-Scale Self-Supervised Robot Learning
Chelsea Finn
Challenges for human-level learning in Deep RL
Josh Tenenbaum
Task Generalization via Deep Reinforcement Learning
Junhyuk Oh
Matko Bošnjak · Nando de Freitas · Tejas D Kulkarni · Arvind Neelakantan · Scott E Reed · Sebastian Riedel · Tim Rocktäschel
What use is Abstraction in Deep Program Induction?
Stephen Muggleton
In Search of Strong Generalization: Building Structured Models in the Age of Neural Networks
Daniel Tarlow
Learning Program Representation: Symbols to Semantics
Charles Sutton
From temporal abstraction to programs
Doina Precup
Learning to Compose by Delegation
Rob Fergus
How Can We Write Large Programs without Thinking?
Percy Liang
Program Synthesis and Machine Learning
Martin Vechev
Limitations of RNNs: a computational perspective
Ed Grefenstette
Learning how to Learn Learning Algorithms: Recursive Self-Improvement
Jürgen Schmidhuber
Bayesian program learning: Prospects for building more human-like AI systems
Joshua Tenenbaum & Kevin Ellis
Learning When to Halt With Adaptive Computation Time
Alex Graves
(aka Autodiff Workshop aka Automatic Differentiation)
Alex Wiltschko · Zach DeVito · Frédéric Bastien · Pascal Lamblin
Automatic Differentiation: History and Headroom
Barak A. Pearlmutter
TensorFlow: Future Directions for Simplifying Large-Scale Machine Learning
Jeff Dean
No more mini-languages: The power of autodiffing full-featured Python
David Duvenaud
Credit assignment: beyond backpropagation
Yoshua Bengio
Autodiff writes your exponential family inference code
Matthew Johnson
The tension between convenience and performance in automatic differentiation
Jeffrey M. Siskind
Dylan Hadfield-Menell · Adrian Weller · David Duvenaud · Jacob Steinhardt · Percy S Liang
Jacob Steinhardt
Rules for Reliable Machine Learning
Martin A Zinkevich
What's your ML Test Score? A rubric for ML production systems
Eric Breck, Shanqing Cai, Eric Nielsen, Michael Salib, D. Sculley
Robust Learning and Inference
Yishay Mansour
Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition
Jennifer Hill
Robust Covariate Shift Classification Using Multiple Feature Views
Anqi Liu, Hong Wang Brian D. Ziebart
Moses Charikar, Jacob Steinhardt, Gregory Valiant Doug Tygar
Adversarial Examples and Adversarial Training
Ian Goodfellow
Summoning Demons: The Pursuit of Exploitable Bugs in Machine Learning
Rock Stevens, Octavian Suciu, Andrew Ruef, Sanghyun Hong, Michael Hicks, Tudor Dumitras
Learning Reliable Objectives
Anca Dragan
Building and Validating the AI behind the Next-Generation Aircraft Collision Avoidance System
Mykel J Kochenderfer
Online Prediction with Selfish Experts
Okke Schrijvers
TensorFlow Debugger: Debugging Dataflow Graphs for Machine Learning
Shanqing Cai, Eric Breck, Eric Nielsen, Michael Salib, D. Sculley
Tomas Mikolov · Baroni Marco · Armand Joulin · Germán Kruszewski · Angeliki Lazaridou · Klemen Simonic
A roadmap for communication-based AI
Marco Baroni
The commAI-env environment for communication-based AI
Allan Jabri
Human-like dialogue: Key challenges for AI
Raquel Fernandez
Learning incrementally to become a general problem solver
Jürgen Schmidhuber
From particular to general: A preliminary case study of transfer learning in reading comprehension
Rudolf Kadlec, Ondrej Bajgar, Jan Kleindienst
Consolidating the search for general AI
Marek Rosa, Jan Feyereisl
Gaining insights from game theory about the emergence of communication
Alex Peysakhovich
Socially constructed machine intelligence
Tomo Lazovich, Matthew C. Graham, Troy M. Lau, Joshua C. Poore
Virtual embodiment: A scalable long-term strategy for Artificial Intelligence research
Douwe Kiela, Luana Bulat, Anita L. Vero, Stephen Clark
Building machines that learn and think like people
Brenden Lake
Malmo: Flexible and scalable evaluation in Minecraft
Fernando Diaz
A paradigm for situated and goal-driven language learning
Jon Gauthier, Igor Mordatch
In praise of fake AI
Arthur Szlam
An evolutionary perspective on machine intelligence
Emmanuel Dupoux
Julian Togelius
Minimally naturalistic Artificial Intelligence
Steven Hansen
Gemma Boleda
Richard Baraniuk · Jiquan Ngiam · Christoph Studer · Phillip Grimaldi · Andrew Lan
BLAh: Boolean Logic Analysis for Graded Student Response Data
Phil Grimaldi, OpenStax/Rice University
Eliminating testing through continuous assessment
Steve Ritter, Carnegie Learning
Pieter Abbeel, UC Berkeley
Mihaela van der Schaar, UCLA
Machine Learning Challenges and Opportunities in MOOCs
Zhenghao Chen, Coursera
Understanding Engagement and Sentiment in MOOCs using Probabilistic Soft Logic (PSL)
Lise Getoor, UC Santa Cruz
Kangwook Lee, KAIST
Using Computational Methods to Improve Feedback for Learners
Anna Rafferty, Carleton College
Estimating student proficiency: Deep learning is not the panacea
Michael Mozer, CU Boulder
Modeling skill interactions with multilayer item response functions
Yan Karklin, Knewton
On Crowdlearning: How do People Learn in the Wild?
Utkarsh Upadhyay, MPI-SWS
Beyond Assessment Scores: How Behavior Can Give Insight into Knowledge Transfer
Christopher Brinton, Zoomi
Using Old Data To Yield Better Personalized Tutoring Systems
Emma Brunskill, CMU