This week, Long Beach, California hosts the 31st annual Conference on Neural Information Processing Systems (NIPS 2017), a machine learning and computational neuroscience conference that includes invited talks, demonstrations and presentations of some of the latest in machine learning research. Google will have a strong presence at NIPS 2017, with over 450 Googlers attending to contribute to, and learn from, the broader academic research community via technical talks and posters, workshops, competitions and tutorials.
Google is at the forefront of machine learning, actively exploring virtually all aspects of the field from classical algorithms to deep learning and more. Focusing on both theory and application, much of our work on language understanding, speech, translation, visual processing, and prediction relies on state-of-the-art techniques that push the boundaries of what is possible. In all of those tasks and many others, we develop learning approaches to understand and generalize, providing us with new ways of looking at old problems and helping transform how we work and live.
If you are attending NIPS 2017, 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 in the list below (Googlers highlighted in blue).
Google is a Platinum Sponsor of NIPS 2017.
Organizing Committee
Program Chair: Samy Bengio
Senior Area Chairs include: Corinna Cortes, Dale Schuurmans, Hugo Larochelle
Area Chairs include: Afshin Rostamizadeh, Amir Globerson, Been Kim, D. Sculley, Dumitru Erhan, Gal Chechik, Hartmut Neven, Honglak Lee, Ian Goodfellow, Jasper Snoek, John Wright, Jon Shlens, Kun Zhang, Lihong Li, Maya Gupta, Moritz Hardt, Navdeep Jaitly, Ryan Adams, Sally Goldman, Sanjiv Kumar, Surya Ganguli, Tara Sainath, Umar Syed, Viren Jain, Vitaly Kuznetsov
Invited Talk
Powering the next 100 years
John Platt
Accepted Papers
A Meta-Learning Perspective on Cold-Start Recommendations for Items
Manasi Vartak, Hugo Larochelle, Arvind Thiagarajan
AdaGAN: Boosting Generative Models
Ilya Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, Bernhard Schölkopf
Deep Lattice Networks and Partial Monotonic Functions
Seungil You, David Ding, Kevin Canini, Jan Pfeifer, Maya Gupta
From which world is your graph
Cheng Li, Varun Kanade, Felix MF Wong, Zhenming Liu
Hiding Images in Plain Sight: Deep Steganography
Shumeet Baluja
Improved Graph Laplacian via Geometric Self-Consistency
Dominique Joncas, Marina Meila, James McQueen
Model-Powered Conditional Independence Test
Rajat Sen, Ananda Theertha Suresh, Karthikeyan Shanmugam, Alexandros Dimakis, Sanjay Shakkottai
Nonlinear random matrix theory for deep learning
Jeffrey Pennington, Pratik Worah
Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice
Jeffrey Pennington, Samuel Schoenholz, Surya Ganguli
SGD Learns the Conjugate Kernel Class of the Network
Amit Daniely
SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability
Maithra Raghu, Justin Gilmer, Jason Yosinski, Jascha Sohl-Dickstein
Learning Hierarchical Information Flow with Recurrent Neural Modules
Danijar Hafner, Alexander Irpan, James Davidson, Nicolas Heess
Online Learning with Transductive Regret
Scott Yang, Mehryar Mohri
Acceleration and Averaging in Stochastic Descent Dynamics
Walid Krichene, Peter Bartlett
Parameter-Free Online Learning via Model Selection
Dylan J Foster, Satyen Kale, Mehryar Mohri, Karthik Sridharan
Dynamic Routing Between Capsules
Sara Sabour, Nicholas Frosst, Geoffrey E Hinton
Modulating early visual processing by language
Harm de Vries, Florian Strub, Jeremie Mary, Hugo Larochelle, Olivier Pietquin, Aaron C Courville
MarrNet: 3D Shape Reconstruction via 2.5D Sketches
Jiajun Wu, Yifan Wang, Tianfan Xue, Xingyuan Sun, Bill Freeman, Josh Tenenbaum
Affinity Clustering: Hierarchical Clustering at Scale
Mahsa Derakhshan, Soheil Behnezhad, Mohammadhossein Bateni, Vahab Mirrokni, MohammadTaghi Hajiaghayi, Silvio Lattanzi, Raimondas Kiveris
Asynchronous Parallel Coordinate Minimization for MAP Inference
Ofer Meshi, Alexander Schwing
Cold-Start Reinforcement Learning with Softmax Policy Gradient
Nan Ding, Radu Soricut
Filtering Variational Objectives
Chris J Maddison, Dieterich Lawson, George Tucker, Mohammad Norouzi, Nicolas Heess, Andriy Mnih, Yee Whye Teh, Arnaud Doucet
Multi-Armed Bandits with Metric Movement Costs
Tomer Koren, Roi Livni, Yishay Mansour
Multiscale Quantization for Fast Similarity Search
Xiang Wu, Ruiqi Guo, Ananda Theertha Suresh, Sanjiv Kumar, Daniel Holtmann-Rice, David Simcha, Felix Yu
Reducing Reparameterization Gradient Variance
Andrew Miller, Nicholas Foti, Alexander D'Amour, Ryan Adams
Statistical Cost Sharing
Eric Balkanski, Umar Syed, Sergei Vassilvitskii
The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings
Krzysztof Choromanski, Mark Rowland, Adrian Weller
Value Prediction Network
Junhyuk Oh, Satinder Singh, Honglak Lee
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models
George Tucker, Andriy Mnih, Chris J Maddison, Dieterich Lawson, Jascha Sohl-Dickstein
Approximation and Convergence Properties of Generative Adversarial Learning
Shuang Liu, Olivier Bousquet, Kamalika Chaudhuri
Attention is All you Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, Illia Polosukhin
PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference
Jonathan Huggins, Ryan Adams, Tamara Broderick
Repeated Inverse Reinforcement Learning
Kareem Amin, Nan Jiang, Satinder Singh
Fair Clustering Through Fairlets
Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, Sergei Vassilvitskii
Affine-Invariant Online Optimization and the Low-rank Experts Problem
Tomer Koren, Roi Livni
Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models
Sergey Ioffe
Bridging the Gap Between Value and Policy Based Reinforcement Learning
Ofir Nachum, Mohammad Norouzi, Kelvin Xu, Dale Schuurmans
Discriminative State Space Models
Vitaly Kuznetsov, Mehryar Mohri
Dynamic Revenue Sharing
Santiago Balseiro, Max Lin, Vahab Mirrokni, Renato Leme, Song Zuo
Multi-view Matrix Factorization for Linear Dynamical System Estimation
Mahdi Karami, Martha White, Dale Schuurmans, Csaba Szepesvari
On Blackbox Backpropagation and Jacobian Sensing
Krzysztof Choromanski, Vikas Sindhwani
On the Consistency of Quick Shift
Heinrich Jiang
Revenue Optimization with Approximate Bid Predictions
Andres Munoz, Sergei Vassilvitskii
Shape and Material from Sound
Zhoutong Zhang, Qiujia Li, Zhengjia Huang, Jiajun Wu, Josh Tenenbaum, Bill Freeman
Learning to See Physics via Visual De-animation
Jiajun Wu, Erika Lu, Pushmeet Kohli, Bill Freeman, Josh Tenenbaum
Conference Demos
Electronic Screen Protector with Efficient and Robust Mobile Vision
Hee Jung Ryu, Florian Schroff
Magenta and deeplearn.js: Real-time Control of DeepGenerative Music Models in the Browser
Curtis Hawthorne, Ian Simon, Adam Roberts, Jesse Engel, Daniel Smilkov, Nikhil Thorat, Douglas Eck
Workshops
6th Workshop on Automated Knowledge Base Construction (AKBC) 2017
Program Committee includes: Arvind Neelakanta
Authors include: Jiazhong Nie, Ni Lao
Acting and Interacting in the Real World: Challenges in Robot Learning
Invited Speakers include: Pierre Sermanet
Advances in Approximate Bayesian Inference
Panel moderator: Matthew D. Hoffman
Conversational AI - Today's Practice and Tomorrow's Potential
Invited Speakers include: Matthew Henderson, Dilek Hakkani-Tur
Organizers include: Larry Heck
Extreme Classification: Multi-class and Multi-label Learning in Extremely Large Label Spaces
Invited Speakers include: Ed Chi, Mehryar Mohri
Learning in the Presence of Strategic Behavior
Invited Speakers include: Mehryar Mohri
Presenters include: Andres Munoz Medina, Sebastien Lahaie, Sergei Vassilvitskii, Balasubramanian Sivan
Learning on Distributions, Functions, Graphs and Groups
Invited speakers include: Corinna Cortes
Machine Deception
Organizers include: Ian Goodfellow
Invited Speakers include: Jacob Buckman, Aurko Roy, Colin Raffel, Ian Goodfellow
Machine Learning and Computer Security
Invited Speakers include: Ian Goodfellow
Organizers include: Nicolas Papernot
Authors include: Jacob Buckman, Aurko Roy, Colin Raffel, Ian Goodfellow
Machine Learning for Creativity and Design
Keynote Speakers include: Ian Goodfellow
Organizers include: Doug Eck, David Ha
Machine Learning for Audio Signal Processing (ML4Audio)
Authors include: Aren Jansen, Manoj Plakal, Dan Ellis, Shawn Hershey, Channing Moore, Rif A. Saurous, Yuxuan Wang, RJ Skerry-Ryan, Ying Xiao, Daisy Stanton, Joel Shor, Eric Batternberg, Rob Clark
Machine Learning for Health (ML4H)
Organizers include: Jasper Snoek, Alex Wiltschko
Keynote: Fei-Fei Li
NIPS Time Series Workshop 2017
Organizers include: Vitaly Kuznetsov
Authors include: Brendan Jou
OPT 2017: Optimization for Machine Learning
Organizers include: Sashank Reddi
ML Systems Workshop
Invited Speakers include: Rajat Monga, Alexander Mordvintsev, Chris Olah, Jeff Dean
Authors include: Alex Beutel, Tim Kraska, Ed H. Chi, D. Scully, Michael Terry
Aligned Artificial Intelligence
Invited Speakers include: Ian Goodfellow
Bayesian Deep Learning
Organizers include: Kevin Murphy
Invited speakers include: Nal Kalchbrenner, Matthew D. Hoffman
BigNeuro 2017
Invited speakers include: Viren Jain
Cognitively Informed Artificial Intelligence: Insights From Natural Intelligence
Authors include: Jiazhong Nie, Ni Lao
Deep Learning At Supercomputer Scale
Organizers include: Erich Elsen, Zak Stone, Brennan Saeta, Danijar Haffner
Deep Learning: Bridging Theory and Practice
Invited Speakers include: Ian Goodfellow
Interpreting, Explaining and Visualizing Deep Learning
Invited Speakers include: Been Kim, Honglak Lee
Authors include: Pieter Kinderman, Sara Hooker, Dumitru Erhan, Been Kim
Learning Disentangled Features: from Perception to Control
Organizers include: Honglak Lee
Authors include: Jasmine Hsu, Arkanath Pathak, Abhinav Gupta, James Davidson, Honglak Lee
Learning with Limited Labeled Data: Weak Supervision and Beyond
Invited Speakers include: Ian Goodfellow
Machine Learning on the Phone and other Consumer Devices
Invited Speakers include: Rajat Monga
Organizers include: Hrishikesh Aradhye
Authors include: Suyog Gupta, Sujith Ravi
Optimal Transport and Machine Learning
Organizers include: Olivier Bousquet
The future of gradient-based machine learning software & techniques
Organizers include: Alex Wiltschko, Bart van Merriënboer
Workshop on Meta-Learning
Organizers include: Hugo Larochelle
Panelists include: Samy Bengio
Symposiums
Deep Reinforcement Learning Symposium
Authors include: Benjamin Eysenbach, Shane Gu, Julian Ibarz, Sergey Levine
Interpretable Machine Learning
Authors include: Minmin Chen
Metalearning
Organizers include: Quoc V Le
Competitions
Adversarial Attacks and Defences
Organizers include: Alexey Kurakin, Ian Goodfellow, Samy Bengio
Competition IV: Classifying Clinically Actionable Genetic Mutations
Organizers include: Wendy Kan
Tutorial
Fairness in Machine Learning
Solon Barocas, Moritz Hardt
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