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The latest from Google Research
NIPS 2016 & Research at Google
domingo, 4 de diciembre de 2016
Posted by Doug Eck, Research Scientist, Google Brain Team
This week, Barcelona hosts the
30
th
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 Jaitly
,
Hugo 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 Krishnan
,
Dumitru 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
Supervision
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
Demonstrations
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
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