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Research at Google and ICLR 2016

Posted by Dumitru Erhan, Gentleman Scientist

This week, San Juan, Puerto Rico hosts the 4th International Conference on Learning Representations (ICLR 2016), 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 2016, Google will have a strong presence with over 40 researchers attending (many from the Google Brain team and Google DeepMind ), 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 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. You can also learn more about our research being presented at ICLR 2016 in the list below (Googlers highlighted in blue ).

Organizing Committee

Program Chairs

Samy Bengio , Brian Kingsbury

Area Chairs include:

John Platt , Tara Sanaith

Oral Sessions

Neural Programmer-Interpreters (Best Paper Award Recipient)

Scott Reed, Nando de Freitas

Net2Net: Accelerating Learning via Knowledge Transfer

Tianqi Chen, Ian Goodfellow , Jon Shlens

Conference Track Posters


Prioritized Experience Replay

Tom Schau ,  John Quan ,  Ioannis Antonoglou ,  David Silver


Reasoning about Entailment with Neural Attention

Tim Rocktäschel, Edward Grefenstette Karl Moritz Hermann ,  Tomáš Kočiský ,  Phil Blunsom

Neural Programmer: Inducing Latent Programs With Gradient Descent

Arvind Neelakantan, Quoc Le ,  Ilya Sutskever


MuProp: Unbiased Backpropagation For Stochastic Neural Networks

Shixiang Gu, Sergey Levine ,  Ilya Sutskever ,  Andriy Mnih


Multi-Task Sequence to Sequence Learning

Minh-Thang Luong, Quoc Le Ilya Sutskever ,  Oriol Vinyals ,  Lukasz Kaiser

A Test of Relative Similarity for Model Selection in Generative Models

Eugene Belilovsky, Wacha Bounliphone, Matthew Blaschko, Ioannis Antonoglou , Arthur Gretton

Continuous control with deep reinforcement learning

Timothy Lillicrap ,  Jonathan Hunt Alexander Pritzel ,  Nicolas Heess ,  Tom Erez ,  Yuval Tassa ,  David Silver ,  Daan Wierstra

Policy Distillation

Andrei Rusu ,  Sergio Gomez ,   Caglar Gulcehre, Guillaume Desjardins ,  James Kirkpatrick ,  Razvan Pascanu ,  Volodymyr Mnih ,  Koray Kavukcuoglu ,  Raia Hadsell

Neural Random-Access Machines

Karol Kurach ,  Marcin Andrychowicz ,  Ilya Sutskever

Variable Rate Image Compression with Recurrent Neural Networks

George Toderici ,  Sean O’Malley ,  Damien Vincent ,  Sung Jin Hwang ,  Michele Covell ,  Shumeet Baluja ,  Rahul Sukthankar ,  David Minnen

Order Matters: Sequence to Sequence for Sets

Oriol Vinyals ,  Samy Bengio ,  Manjunath Kudlur


Grid Long Short-Term Memory

Nal Kalchbrenner ,  Alex Graves ,  Ivo Danihelka


Neural GPUs Learn Algorithms

Lukasz Kaiser ,  Ilya Sutskever

ACDC: A Structured Efficient Linear Layer

Marcin Moczulski, Misha Denil , Jeremy Appleyard, Nando de Freitas

Workshop Track Posters


Revisiting Distributed Synchronous SGD

Jianmin Chen ,  Rajat Monga ,  Samy Bengio ,  Rafal Jozefowicz

Black Box Variational Inference for State Space Models

Evan Archer, Il Memming Park, Lars Buesing , John Cunningham, Liam Paninski


A Minimalistic Approach to Sum-Product Network Learning for Real Applications

Viktoriya Krakovna, Moshe Looks

Efficient Inference in Occlusion-Aware Generative Models of Images

Jonathan Huang , Kevin Murphy


Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

Christian Szegedy ,  Sergey Ioffe ,  Vincent Vanhoucke

Deep Autoresolution Networks

Gabriel Pereyra , Christian Szegedy

Learning visual groups from co-occurrences in space and time

Phillip Isola, Daniel Zoran, Dilip Krishnan , Edward H. Adelson

Adding Gradient Noise Improves Learning For Very Deep Networks

Arvind Neelakantan, Luke Vilnis, Quoc V. Le ,  Ilya Sutskever ,  Lukasz Kaiser ,  Karol Kurach , James Martens

Adversarial Autoencoders

Alireza Makhzani, Jonathon Shlens ,  Navdeep Jaitly ,  Ian Goodfellow

Generating Sentences from a Continuous Space

Samuel R. Bowman, Luke Vilnis, Oriol Vinyals ,  Andrew M. Dai ,  Rafal Jozefowicz ,  Samy Bengio

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