NIPS 2016 & Research at Google

December 5, 2016

Posted by Doug Eck, Research Scientist, Google Brain Team

This week, Barcelona hosts the 30th Annual Conference on Neural Information Processing Systems (NIPS 2016), a machine learning and computational neuroscience conference that includes invited talks, demonstrations and oral and poster presentations of some of the latest in machine learning research. Google will have a strong presence at NIPS 2016, with over 280 Googlers attending in order to contribute to and learn from the broader academic research community by presenting technical talks and posters, in addition to hosting workshops and tutorials.

Research at Google is at the forefront of innovation in Machine Intelligence, actively exploring virtually all aspects of machine learning including classical algorithms as well as cutting-edge techniques such as deep learning. Focusing on both theory as well as application, much of our work on language understanding, speech, translation, visual processing, ranking, and prediction relies on Machine Intelligence. In all of those tasks and many others, we gather large volumes of direct or indirect evidence of relationships of interest, and develop learning approaches to understand and generalize.

If you are attending NIPS 2016, we hope you’ll stop by our booth and chat with our researchers about the projects and opportunities at Google that go into solving interesting problems for billions of people, and to see demonstrations of some of the exciting research we pursue. You can also learn more about our work being presented at NIPS 2016 in the list below (Googlers highlighted in blue).

Google is a Platinum Sponsor of NIPS 2016.

Organizing Committee
Executive Board includes: Corinna Cortes, Fernando Pereira
Advisory Board includes: John C. Platt
Area Chairs include: John Shlens, Moritz Hardt, Navdeep JaitlyHugo Larochelle, Honglak Lee, Sanjiv Kumar, Gal Chechik

Invited Talk
Dynamic Legged Robots
Marc Raibert

Accepted Papers:
Boosting with Abstention
Corinna Cortes, Giulia DeSalvo, Mehryar Mohri

Community Detection on Evolving Graphs
Stefano Leonardi, Aris Anagnostopoulos, Jakub Łącki, Silvio Lattanzi, Mohammad Mahdian

Linear Relaxations for Finding Diverse Elements in Metric Spaces
Aditya Bhaskara, Mehrdad Ghadiri, Vahab Mirrokni, Ola Svensson

Nearly Isometric Embedding by Relaxation
James McQueen, Marina Meila, Dominique Joncas

Optimistic Bandit Convex Optimization
Mehryar Mohri, Scott Yang

Reward Augmented Maximum Likelihood for Neural Structured Prediction
Mohammad Norouzi, Samy Bengio, Zhifeng Chen, Navdeep Jaitly, Mike Schuster, Yonghui Wu, Dale Schuurmans

Stochastic Gradient MCMC with Stale Gradients
Changyou Chen, Nan Ding, Chunyuan Li, Yizhe Zhang, Lawrence Carin

Unsupervised Learning for Physical Interaction through Video Prediction
Chelsea Finn*, Ian Goodfellow, Sergey Levine

Using Fast Weights to Attend to the Recent Past
Jimmy Ba, Geoffrey Hinton, Volodymyr Mnih, Joel Leibo, Catalin Ionescu

A Credit Assignment Compiler for Joint Prediction
Kai-Wei Chang, He He, Stephane Ross, Hal III

A Neural Transducer
Navdeep Jaitly, Quoc Le, Oriol Vinyals, Ilya Sutskever, David Sussillo, Samy Bengio

Attend, Infer, Repeat: Fast Scene Understanding with Generative Models
S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, Koray Kavukcuoglu, Geoffrey Hinton

Bi-Objective Online Matching and Submodular Allocations
Hossein Esfandiari, Nitish Korula, Vahab Mirrokni

Combinatorial Energy Learning for Image Segmentation
Jeremy Maitin-Shepard, Viren Jain, Michal Januszewski, Peter Li, Pieter Abbeel

Deep Learning Games
Dale Schuurmans, Martin Zinkevich

DeepMath - Deep Sequence Models for Premise Selection
Geoffrey Irving, Christian Szegedy, Niklas Een, Alexander Alemi, François Chollet, Josef Urban

Density Estimation via Discrepancy Based Adaptive Sequential Partition
Dangna Li, Kun Yang, Wing Wong

Domain Separation Networks
Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman Dilip KrishnanDumitru Erhan

Fast Distributed Submodular Cover: Public-Private Data Summarization
Baharan Mirzasoleiman, Morteza Zadimoghaddam, Amin Karbasi

Satisfying Real-world Goals with Dataset Constraints
Gabriel Goh, Andrew Cotter, Maya Gupta, Michael P Friedlander

Can Active Memory Replace Attention?
Łukasz Kaiser, Samy Bengio

Fast and Flexible Monotonic Functions with Ensembles of Lattices
Kevin Canini Andy Cotter Maya Gupta Mahdi Fard Jan Pfeifer

Launch and Iterate: Reducing Prediction Churn
Quentin Cormier, Mahdi Fard, Kevin Canini, Maya Gupta

On Mixtures of Markov Chains
Rishi Gupta, Ravi Kumar, Sergei Vassilvitskii

Orthogonal Random Features
Felix Xinnan Yu Ananda Theertha Suresh Krzysztof Choromanski Dan Holtmann-Rice
Sanjiv Kumar

Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D
Xinchen Yan, Jimei Yang, Ersin Yumer, Yijie Guo, Honglak Lee

Structured Prediction Theory Based on Factor Graph Complexity
Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Scott Yang

Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity
Amit Daniely, Roy Frostig, Yoram Singer

Interactive musical improvisation with Magenta
Adam Roberts, Sageev Oore, Curtis Hawthorne, Douglas Eck

Content-based Related Video Recommendation
Joonseok Lee

Workshops, Tutorials and Symposia
Advances in Approximate Bayesian Inference
Advisory Committee includes: Kevin P. Murphy
Invited Speakers include: Matt Johnson
Panelists include: Ryan Sepassi

Adversarial Training
Accepted Authors: Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein, Augustus Odena, Christopher Olah, Jonathon Shlens

Bayesian Deep Learning
Organizers include: Kevin P. Murphy
Accepted Authors include: Rif A. Saurous, Eugene Brevdo, Kevin Murphy, Eric Jang, Shixiang Gu, Ben Poole

Brains & Bits: Neuroscience Meets Machine Learning
Organizers include: Jascha Sohl-Dickstein

Connectomics II: Opportunities & Challanges for Machine Learning
Organizers include: Viren Jain

Constructive Machine Learning
Invited Speakers include: Douglas Eck

Continual Learning & Deep Networks
Invited Speakers include: Honglak Lee

Deep Learning for Action & Interaction
Organizers include: Sergey Levine
Invited Speakers include: Honglak Lee
Accepted Authors include: Pararth Shah, Dilek Hakkani-Tur, Larry Heck

End-to-end Learning for Speech and Audio Processing
Invited Speakers include: Tara Sainath
Accepted Authors include: Brian Patton, Yannis Agiomyrgiannakis, Michael Terry, Kevin Wilson, Rif A. Saurous, D. Sculley

Extreme Classification: Multi-class & Multi-label Learning in Extremely Large Label Spaces
Organizers include: Samy Bengio

Interpretable Machine Learning for Complex Systems
Invited Speaker: Honglak Lee
Accepted Authors include: Daniel Smilkov, Nikhil Thorat, Charles Nicholson, Emily Reif, Fernanda Viegas, Martin Wattenberg

Large Scale Computer Vision Systems
Organizers include: Gal Chechik

Machine Learning Systems
Invited Speakers include: Jeff Dean

Nonconvex Optimization for Machine Learning: Theory & Practice
Organizers include: Hossein Mobahi

Optimizing the Optimizers
Organizers include: Alex Davies

Reliable Machine Learning in the Wild
Accepted Authors: Andres Medina, Sergei Vassilvitskii

The Future of Gradient-Based Machine Learning Software
Invited Speakers: Jeff Dean, Matt Johnson

Time Series Workshop
Organizers include: Vitaly Kuznetsov
Invited Speakers include: Mehryar Mohri

Theory and Algorithms for Forecasting Non-Stationary Time Series
Tutorial Organizers: Vitaly Kuznetsov, Mehryar Mohri

Women in Machine Learning
Invited Speakers include: Maya Gupta

* Work done as part of the Google Brain team