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Research at Google and ICLR 2017
Monday, April 24, 2017
Posted by Ian Goodfellow, Staff Research Scientist, Google Brain Team
This week, Toulon, France hosts the
5th International Conference on Learning Representations
(ICLR 2017), a conference focused on how one can learn meaningful and useful representations of data for
Machine Learning
. ICLR includes conference and workshop tracks, with invited talks along with oral and poster presentations of some of the latest research on deep learning, metric learning, kernel learning, compositional models, non-linear structured prediction, and issues regarding non-convex optimization.
At the forefront of innovation in cutting-edge technology in
Neural Networks
and
Deep Learning
, Google focuses on both theory and application, developing learning approaches to understand and generalize. As Platinum Sponsor of ICLR 2017, Google will have a strong presence with over 50 researchers attending (many from the
Google Brain team
and
Google Research Europe
), contributing to and learning from the broader academic research community by presenting papers and posters, in addition to participating on organizing committees and in workshops.
If you are attending ICLR 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. You can also learn more about our research being presented at ICLR 2017 in the list below (Googlers highlighted in
blue
).
Area Chairs include:
George Dahl
,
Slav Petrov
,
Vikas Sindhwani
Program Chairs include:
Hugo Larochelle
,
Tara Sainath
Contributed Talks
Understanding Deep Learning Requires Rethinking Generalization
(Best Paper Award)
Chiyuan Zhang*,
Samy Bengio
,
Moritz Hardt
, Benjamin Recht*, Oriol Vinyals
Semi-Supervised Knowledge Transfer for Deep Learning from Private Training Data
(Best Paper Award)
Nicolas Papernot*,
Martín Abadi
,
Úlfar Erlingsson
,
Ian Goodfellow
,
Kunal
Talwar
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic
Shixiang (Shane) Gu*, Timothy Lillicrap, Zoubin Ghahramani, Richard E.
Turner,
Sergey Levine
Neural Architecture Search with Reinforcement Learning
Barret Zoph
,
Quoc Le
Posters
Adversarial Machine Learning at Scale
Alexey Kurakin
,
Ian J. Goodfellow
†
,
Samy Bengio
Capacity and Trainability in Recurrent Neural Networks
Jasmine Collins
,
Jascha Sohl-Dickstein
,
David Sussillo
Improving Policy Gradient by Exploring Under-Appreciated Rewards
Ofir Nachum
,
Mohammad Norouzi
,
Dale Schuurmans
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
Noam Shazeer
,
Azalia Mirhoseini
, Krzysztof Maziarz,
Andy Davis
,
Quoc Le
,
Geoffrey Hinton
,
Jeff Dean
Unrolled Generative Adversarial Networks
Luke Metz
, Ben Poole*, David Pfau,
Jascha Sohl-Dickstein
Categorical Reparameterization with Gumbel-Softmax
Eric Jang
, Shixiang (Shane) Gu*, Ben Poole*
Decomposing Motion and Content for Natural Video Sequence Prediction
Ruben Villegas, Jimei Yang, Seunghoon Hong, Xunyu Lin,
Honglak Lee
Density Estimation Using Real NVP
Laurent Dinh*,
Jascha Sohl-Dickstein
,
Samy Bengio
Latent Sequence Decompositions
William Chan*, Yu Zhang*,
Quoc Le
, Navdeep Jaitly*
Learning a Natural Language Interface with Neural Programmer
Arvind Neelakantan*,
Quoc V. Le
,
Martín Abadi
, Andrew McCallum*, Dario
Amodei*
Deep Information Propagation
Samuel Schoenholz
,
Justin Gilmer
, Surya Ganguli,
Jascha Sohl-Dickstein
Identity Matters in Deep Learning
Moritz Hardt
, Tengyu Ma
A Learned Representation For Artistic Style
Vincent Dumoulin*,
Jonathon Shlens
,
Manjunath Kudlur
Adversarial Training Methods for Semi-Supervised Text Classification
Takeru Miyato
,
Andrew M. Dai
,
Ian Goodfellow
†
HyperNetworks
David Ha
,
Andrew Dai
,
Quoc V. Le
Learning to Remember Rare Events
Lukasz Kaiser
,
Ofir Nachum,
Aurko Roy*,
Samy Bengio
Deep Learning with Dynamic Computation Graphs
Moshe Looks
,
Marcello Herreshof
,
DeLesley Hutchins
,
Peter Norvig
HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving
Cezary Kaliszyk,
François Chollet
,
Christian Szegedy
Hyperband: Bandit-based Configuration Evaluation for Hyperparameter Optimization
Lisha Li, Kevin Jamieson, Giulia DeSalvo,
Afshin Rostamizadeh
, Ameet Talwalkar
Workshop Track Abstracts
Particle Value Functions
Chris J. Maddison,
Dieterich Lawson
,
George Tucker
, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh
Neural Combinatorial Optimization with Reinforcement Learning
Irwan Bello
,
Hieu Pham
,
Quoc V. Le
,
Mohammad Norouzi
,
Samy Bengio
Short and Deep: Sketching and Neural Networks
Amit Daniely
,
Nevena Lazic
,
Yoram Singer
,
Kunal Talwar
Explaining the Learning Dynamics of Direct Feedback Alignment
Justin Gilmer
,
Colin Raffel
,
Samuel S. Schoenholz
,
Maithra Raghu
,
Jascha Sohl-Dickstein
Training a Subsampling Mechanism in Expectation
Colin Raffel
,
Dieterich Lawson
Tuning Recurrent Neural Networks with Reinforcement Learning
Natasha Jaques
*, Shixiang (Shane) Gu*, Richard E. Turner,
Douglas Eck
REBAR: Low-Variance, Unbiased Gradient Estimates for Discrete Latent Variable Models
George Tucker
, Andriy Mnih, Chris J. Maddison,
Jascha Sohl-Dickstein
Adversarial Examples in the Physical World
Alexey Kurakin
,
Ian Goodfellow
†
,
Samy Bengio
Regularizing Neural Networks by Penalizing Confident Output Distributions
Gabriel Pereyra
,
George Tucker
,
Jan Chorowski
,
Lukasz Kaiser
,
Geoffrey Hinton
Unsupervised Perceptual Rewards for Imitation Learning
Pierre Sermanet
,
Kelvin Xu
,
Sergey Levine
Changing Model Behavior at Test-time Using Reinforcement Learning
Augustus Odena
,
Dieterich Lawson
,
Christopher Olah
* Work performed while at Google
† Work performed while at OpenAI
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