Google Research Blog
The latest news from Research at Google
NIPS 2016 & Research at Google
Sunday, December 04, 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
↩
ACL 2016 & Research at Google
Sunday, August 07, 2016
Posted by Slav Petrov, Research Scientist
This week, Berlin hosts the
2016 Annual Meeting of the Association for Computational Linguistics
(ACL 2016), the premier conference of the field of computational linguistics, covering a broad spectrum of diverse research areas that are concerned with computational approaches to natural language. As a leader in
Natural Language Processing
(NLP) and a Platinum Sponsor of the conference, Google will be on hand to showcase research interests that include syntax, semantics, discourse, conversation, multilingual modeling, sentiment analysis, question answering, summarization, and generally building better learners using labeled and unlabeled data, state-of-the-art modeling, and learning from indirect supervision.
Our systems are used in numerous ways across Google, impacting user experience in search, mobile, apps, ads, translate and more. Our work spans the range of traditional NLP tasks, with general-purpose syntax and semantic algorithms underpinning more specialized systems.
Our researchers are experts in natural language processing and machine learning, and combine methodological research with applied science, and our engineers are equally involved in long-term research efforts and driving immediate applications of our technology.
If you’re attending ACL 2016, we hope that you’ll stop by the booth to check out some demos, meet our researchers and discuss projects and opportunities at Google that go into solving interesting problems for billions of people. Learn more about Google research being presented at ACL 2016 below (Googlers highlighted in
blue
), and visit the Natural Language Understanding Team page at
g.co/NLUTeam
.
Papers
Generalized Transition-based Dependency Parsing via Control Parameters
Bernd Bohnet
,
Ryan McDonald
,
Emily Pitler
,
Ji Ma
Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning
Yulia Tsvetkov, Manaal Faruqui,
Wang Ling (Google DeepMind)
,
Chris Dyer (Google DeepMind)
Morpho-syntactic Lexicon Generation Using Graph-based Semi-supervised Learning
(
TACL
)
Manaal Faruqui,
Ryan McDonald
,
Radu Soricut
Many Languages, One Parser
(
TACL
)
Waleed Ammar, George Mulcaire, Miguel Ballesteros,
Chris Dyer (Google DeepMind)
*
, Noah A. Smith
Latent Predictor Networks for Code Generation
Wang Ling (Google DeepMind)
,
Phil Blunsom (Google DeepMind)
,
Edward Grefenstette (Google DeepMind)
,
Karl Moritz Hermann (Google DeepMind)
,
Tomáš Kočiský (Google DeepMind)
,
Fumin Wang (Google DeepMind)
,
Andrew Senior (Google DeepMind)
Collective Entity Resolution with Multi-Focal Attention
Amir Globerson
,
Nevena Lazic
,
Soumen Chakrabarti,
Amarnag Subramanya
,
Michael Ringgaard
,
Fernando Pereira
Plato: A Selective Context Model for Entity Resolution
(
TACL
)
Nevena Lazic
,
Amarnag Subramanya
,
Michael Ringgaard
,
Fernando Pereira
WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia
Daniel Hewlett
,
Alexandre Lacoste
,
Llion Jones
,
Illia Polosukhin
,
Andrew Fandrianto
,
Jay Han
,
Matthew Kelcey
,
David Berthelot
Stack-propagation: Improved Representation Learning for Syntax
Yuan Zhang,
David Weiss
Cross-lingual Models of Word Embeddings: An Empirical Comparison
Shyam Upadhyay, Manaal Faruqui,
Chris Dyer (Google DeepMind)
,
Dan Roth
Globally Normalized Transition-Based Neural Networks
(Outstanding Papers Session)
Daniel Andor
,
Chris Alberti
,
David Weiss
,
Aliaksei Severyn
,
Alessandro Presta
,
Kuzman Ganchev
,
Slav Petrov
,
Michael Collins
Posters
Cross-lingual projection for class-based language models
Beat Gfeller
,
Vlad Schogol
,
Keith Hall
Synthesizing Compound Words for Machine Translation
Austin Matthews,
Eva Schlinger
*
, Alon Lavie,
Chris Dyer (Google DeepMind)
*
Cross-Lingual Morphological Tagging for Low-Resource Languages
Jan Buys,
Jan A. Botha
Workshops
1st Workshop on Representation Learning for NLP
Keynote Speakers include:
Raia Hadsell (Google DeepMind)
Workshop Organizers include:
Edward Grefenstette (Google DeepMind)
,
Phil Blunsom (Google DeepMind)
,
Karl Moritz Hermann (Google DeepMind)
Program Committee members include:
Tomáš Kočiský (Google DeepMind)
,
Wang Ling (Google DeepMind)
,
Ankur Parikh (Google)
,
John Platt (Google)
,
Oriol Vinyals (Google DeepMind)
1st Workshop on Evaluating Vector-Space Representations for NLP
Contributed Papers:
Problems With Evaluation of Word Embeddings Using Word Similarity Tasks
Manaal Faruqui, Yulia Tsvetkov, Pushpendre Rastogi,
Chris Dyer (Google DeepMind)
*
Correlation-based Intrinsic Evaluation of Word Vector Representations
Yulia Tsvetkov, Manaal Faruqui,
Chris Dyer (Google DeepMind)
SIGFSM Workshop on Statistical NLP and Weighted Automata
Contributed Papers:
Distributed representation and estimation of WFST-based n-gram models
Cyril Allauzen
,
Michael Riley
,
Brian Roark
Pynini: A Python library for weighted finite-state grammar compilation
Kyle Gorman
*
Work completed at CMU
↩
CVPR 2016 & Research at Google
Tuesday, June 28, 2016
Posted by Rahul Sukthankar, Research Scientist
This week, Las Vegas hosts the
2016 Conference on Computer Vision and Pattern Recognition
(CVPR 2016), the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. As a leader in computer vision research, Google has a strong presence at CVPR 2016, with many Googlers presenting papers and invited talks at the conference, tutorials and workshops.
We congratulate Google Research Scientist Ce Liu and Google Faculty Advisor
Abhinav Gupta
, who were selected as this year’s recipients of the
PAMI Young Researcher Award
for outstanding research contributions within computer vision. We also congratulate Googler Henrik Stewenius for receiving the
Longuet-Higgins Prize
, a retrospective award that recognizes up to two CVPR papers from ten years ago that have made a significant impact on computer vision research, for his 2006 CVPR paper “
Scalable Recognition with a Vocabulary Tree
”, co-authored with David Nister, during their time at University of Kentucky.
If you are attending CVPR this year, please stop by our booth and chat with our researchers about the projects and opportunities at Google that go into solving interesting problems for hundreds of millions of people. The Google booth will also showcase several recent efforts, including the technology behind
Motion Stills
, a live demo of neural network-based image compression and
TensorFlow-Slim
, the lightweight library for defining, training and evaluating models in TensorFlow. Learn more about our research being presented at CVPR 2016 in the list below (Googlers highlighted in
blue
).
Oral Presentations
Generation and Comprehension of Unambiguous Object Descriptions
Junhua Mao,
Jonathan Huang
,
Alexander Toshev
, Oana Camburu, Alan L. Yuille,
Kevin Murphy
Detecting Events and Key Actors in Multi-Person Videos
Vignesh Ramanathan,
Jonathan Huang
,
Sami Abu-El-Haija
,
Alexander Gorban
,
Kevin Murphy
, Li Fei-Fei
Spotlight Session: 3D Reconstruction
DeepStereo: Learning to Predict New Views From the World’s Imagery
John Flynn,
Ivan Neulander
, James Philbin,
Noah Snavely
Posters
Discovering the Physical Parts of an Articulated Object Class From Multiple Videos
Luca Del Pero,
Susanna Ricco
,
Rahul Sukthankar
, Vittorio Ferrari
Blockout: Dynamic Model Selection for Hierarchical Deep Networks
Calvin Murdock,
Zhen Li
,
Howard Zhou
,
Tom Duerig
Rethinking the Inception Architecture for Computer Vision
Christian Szegedy
,
Vincent Vanhoucke
,
Sergey Ioffe
,
Jon Shlens
, Zbigniew Wojna
Improving the Robustness of Deep Neural Networks via Stability Training
Stephan Zheng,
Yang Song
,
Thomas Leung
,
Ian Goodfellow
Semantic Image Segmentation With Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform
Liang-Chieh Chen,
Jonathan T. Barron
,
George Papandreou
,
Kevin Murphy
, Alan L. Yuille
Tutorial
Optimization Algorithms for Subset Selection and Summarization in Large Data Sets
Ehsan Elhamifar, Jeff Bilmes,
Alex Kulesza
, Michael Gygli
Workshops
Perceptual Organization in Computer Vision: The Role of Feedback in Recognition and Reorganization
Organizers:
Katerina Fragkiadaki
, Phillip Isola,
Joao Carreira
Invited talks:
Viren Jain
,
Jitendra Malik
VQA Challenge Workshop
Invited talks:
Jitendra Malik
,
Kevin Murphy
Women in Computer Vision
Invited talk:
Caroline Pantofaru
Computational Models for Learning Systems and Educational Assessment
Invited talk:
Jonathan Huang
Large-Scale Scene Understanding (LSUN) Challenge
Invited talk:
Jitendra Malik
Large Scale Visual Recognition and Retrieval: BigVision 2016
General Chairs:
Jason Corso, Fei-Fei Li,
Samy Bengio
ChaLearn Looking at People
Invited talk:
Florian Schroff
Medical Computer Vision
Invited talk:
Ramin Zabih
ICML 2016 & Research at Google
Monday, June 20, 2016
Posted by Afshin Rostamizadeh, Research Scientist
This week, New York hosts the
2016 International Conference on Machine Learning
(ICML 2016), a premier annual Machine Learning event supported by the
International Machine Learning Society
(IMLS). Machine Learning is a key focus area at Google, with highly active research groups exploring virtually all aspects of the field, including deep learning and more classical algorithms.
We work on an extremely wide variety of machine learning problems that arise from a broad range of applications at Google. One particularly important setting is that of large-scale learning, where we utilize scalable tools and architectures to build machine learning systems that work with large volumes of data that often preclude the use of standard single-machine training algorithms. In doing so, we are able to solve deep scientific problems and engineering challenges, exploring theory as well as application, in areas of language, speech, translation, music, visual processing and more.
As Gold Sponsor, Google has a strong presence at ICML 2016 with many Googlers publishing their research and hosting workshops. If you’re attending, we hope you’ll visit the Google booth and talk with our researchers to learn more about the exciting work, creativity and fun that goes into solving interesting ML problems that impact millions of people. You can also learn more about our research being presented at ICML 2016 in the list below (Googlers highlighted in
blue
).
ICML 2016 Organizing Committee
Area Chairs include:
Corinna Cortes
,
John Blitzer
,
Maya Gupta
,
Moritz Hardt
,
Samy Bengio
IMLS
Board Members include:
Corinna Cortes
Accepted Papers
ADIOS: Architectures Deep In Output Space
Moustapha Cisse, Maruan Al-Shedivat,
Samy Bengio
Associative Long Short-Term Memory
Ivo Danihelka (Google DeepMind)
,
Greg Wayne
(Google DeepMind)
,
Benigno Uria
(Google DeepMind)
,
Nal Kalchbrenner
(Google DeepMind)
,
Alex Graves
(Google DeepMind)
Asynchronous Methods for Deep Reinforcement Learning
Volodymyr Mnih
(Google DeepMind)
,
Adria Puigdomenech Badia
(Google DeepMind)
, Mehdi Mirza,
Alex Graves
(Google DeepMind)
,
Timothy Lillicrap
(Google DeepMind)
,
Tim Harley
(Google DeepMind)
,
David Silver
(Google DeepMind)
,
Koray Kavukcuoglu
(Google DeepMind)
Binary embeddings with structured hashed projections
Anna Choromanska,
Krzysztof Choromanski
, Mariusz Bojarski, Tony Jebara,
Sanjiv Kumar
, Yann LeCun
Discrete Distribution Estimation Under Local Privacy
Peter Kairouz,
Keith Bonawitz
,
Daniel Ramage
Dueling Network Architectures for Deep Reinforcement Learning
(Best Paper Award recipient)
Ziyu Wang
(Google DeepMind)
,
Nando de Freitas
(Google DeepMind)
,
Tom Schaul
(Google DeepMind)
,
Matteo Hessel
(Google DeepMind)
,
Hado van Hasselt
(Google DeepMind)
,
Marc Lanctot
(Google DeepMind)
Exploiting Cyclic Symmetry in Convolutional Neural Networks
Sander Dieleman
(Google DeepMind)
,
Jeffrey De Fauw
(Google DeepMind)
,
Koray Kavukcuoglu
(Google DeepMind)
Fast Constrained Submodular Maximization: Personalized Data Summarization
Baharan Mirzasoleiman,
Ashwinkumar Badanidiyuru
, Amin Karbasi
Greedy Column Subset Selection: New Bounds and Distributed Algorithms
Jason Altschuler, Aditya Bhaskara,
Gang Fu
,
Vahab Mirrokni
,
Afshin Rostamizadeh
,
Morteza Zadimoghaddam
Horizontally Scalable Submodular Maximization
Mario Lucic, Olivier Bachem,
Morteza Zadimoghaddam
, Andreas Krause
Continuous Deep Q-Learning with Model-based Acceleration
Shixiang Gu,
Timothy Lillicrap
(Google DeepMind)
,
Ilya Sutskever
,
Sergey Levine
Meta-Learning with Memory-Augmented Neural Networks
Adam Santoro
(Google DeepMind)
, Sergey Bartunov,
Matthew Botvinick
(Google DeepMind)
,
Daan Wierstra
(Google DeepMind)
,
Timothy Lillicrap
(Google DeepMind)
One-Shot Generalization in Deep Generative Models
Danilo Rezende
(Google DeepMind)
,
Shakir Mohamed
(Google DeepMind)
,
Daan Wierstra
(Google DeepMind)
Pixel Recurrent Neural Networks
(Best Paper Award recipient)
Aaron Van den Oord
(Google DeepMind)
,
Nal Kalchbrenner
(Google DeepMind)
,
Koray Kavukcuoglu
(Google DeepMind)
Pricing a low-regret seller
Hoda Heidari,
Mohammad Mahdian
,
Umar Syed
,
Sergei Vassilvitskii
, Sadra Yazdanbod
Primal-Dual Rates and Certificates
Celestine Dünner,
Simone Forte
, Martin Takac, Martin Jaggi
Recommendations as Treatments: Debiasing Learning and Evaluation
Tobias Schnabel, Thorsten Joachims, Adith Swaminathan, Ashudeep Singh,
Navin Chandak
Recycling Randomness with Structure for Sublinear Time Kernel Expansions
Krzysztof Choromanski
,
Vikas Sindhwani
Train faster, generalize better: Stability of stochastic gradient descent
Moritz Hardt
, Ben Recht,
Yoram Singer
Variational Inference for Monte Carlo Objectives
Andriy Mnih
(Google DeepMind)
,
Danilo Rezende
(Google DeepMind)
Workshops
Abstraction in Reinforcement Learning
Organizing Committee:
Daniel Mankowitz,
Timothy Mann
(Google DeepMind)
, Shie Mannor
Invited Speaker:
David Silver
(Google DeepMind)
Deep Learning Workshop
Organizers:
Antoine Bordes, Kyunghyun Cho, Emily Denton,
Nando de Freitas
(Google DeepMind)
, Rob Fergus
Invited Speaker:
Raia Hadsell
(Google DeepMind)
Neural Networks Back To The Future
Organizers:
Léon Bottou, David Grangier, Tomas Mikolov,
John Platt
Data-Efficient Machine Learning
Organizers:
Marc Deisenroth,
Shakir Mohamed
(Google DeepMind)
, Finale Doshi-Velez, Andreas Krause, Max Welling
On-Device Intelligence
Organizers:
Vikas Sindhwani
,
Daniel Ramage
,
Keith Bonawitz
, Suyog Gupta, Sachin Talathi
Invited Speakers:
Hartwig Adam
,
H. Brendan McMahan
Online Advertising Systems
Organizing Committee:
Sharat Chikkerur,
Hossein Azari
, Edoardo Airoldi
Opening Remarks:
Hossein Azari
Invited Speakers:
Martin Pál
,
Todd Phillips
Anomaly Detection 2016
Organizing Committee:
Nico Goernitz, Marius Kloft,
Vitaly Kuznetsov
Tutorials
Deep Reinforcement Learning
David Silver
(Google DeepMind)
Rigorous Data Dredging: Theory and Tools for Adaptive Data Analysis
Moritz Hardt
, Aaron Roth
Research at Google and ICLR 2016
Sunday, May 01, 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
Lessons learned while protecting Gmail
Tuesday, March 29, 2016
Posted by Elie Bursztein - anti-abuse & security research, Nicolas Lidzborski - Gmail security engineering, and Vijay Eranti - Gmail anti-abuse engineering
Earlier this year in San Francisco,
USENIX
hosted their inaugural
Enigma Conference
, which focused on security, privacy and electronic crime through the lens of emerging threats and novel attacks. We were
excited to help make this conference happen
and to participate in it.
At the conference, we heard from a variety of terrific speakers including:
Ron Rivest
, Professor at MIT and inventor of RSA, who spoke about
the consequences of backdooring encryption
Rob Joyce
, Chief of the NSA Tailored Access Operations organization, who spoke about about
defending against state attackers
George “Geohot” Hotz
, Hacker extraordinaire, who discussed
state of the art software debugging
In addition, we were able to
share the lessons we’ve learned
about protecting Gmail users since it was launched over a decade ago. Those lessons are summarized in the infographic below (the talk slides are
also available
).
We were proud to sponsor this year's inaugural Enigma conference, and it is our hope that the core lessons that we have learned over the years can benefit other online products and services. We're looking forward to participating again next year when
Enigma returns in 2017
. We hope to see you there!
Why attend USENIX Enigma?
Monday, January 11, 2016
Parisa Tabriz, Security Princess & Enigma Program Co-Chair
Last August,
we announced USENIX Enigma
, a new conference intended to shine a light on great, thought-provoking research in security, privacy, and electronic crime. With Enigma beginning in just a few short weeks, I wanted to share a couple of the reasons I’m personally excited about this new conference.
Enigma aims to bridge the divide that exists between experts working in academia, industry, and public service, explicitly bringing researchers from different sectors together to share their work. Our speakers include those spearheading the defense of digital rights (
Electronic Frontier Foundation
,
Access Now
), practitioners at a number of well known industry leaders (
Akamai
,
Blackberry
,
Facebook
,
LinkedIn
,
Netflix
,
Twitter
), and researchers from multiple universities in the U.S. and abroad. With the diverse
session topics and organizations represented
, I expect interesting—and perhaps spirited—coffee break and lunchtime discussions among the equally diverse list of conference attendees.
Of course, I’m very proud to have some of my Google colleagues speaking at Enigma:
Adrienne Porter Felt will talk about blending research and engineering to solve usable security problems. You’ll hear how Chrome’s usable security team runs user studies and experiments to motivate engineering and design decisions. Adrienne will share the challenges they’ve faced when trying to adapt existing usable security research to practice, and give insight into how they’ve achieved successes.
Ben Hawkes will be speaking about
Project Zero
, a security research team dedicated to the mission of, “making
0day
hard.” Ben will talk about why Project Zero exists, and some of the recent trends and technologies that make vulnerability discovery and exploitation fundamentally harder.
Kostya Serebryany will be presenting a 3-pronged approach to securing C++ code based on his many years of experiencing wrangling complex, buggy software. Kostya will survey multiple dynamic sanitizing tools him and his team have made publicly available, review control-flow and data-flow guided fuzzing, and explain a method to harden your code in the presence of any bugs that remain.
Elie Bursztein will go through key lessons the Gmail team learned over the past 11 years while protecting users from spam, phishing, malware, and web attacks. Illustrated with concrete numbers and examples from one of the largest email systems on the planet, attendees will gain insight into specific techniques and approaches useful in fighting abuse and securing their online services.
In addition to raw content, my Program Co-Chair,
David Brumley
, and I have prioritized talk quality. Researchers dedicate months or years of their time to thinking about a problem and conducting the technical work of research, but a common criticism of technical conferences is that the actual presentation of that research seems like an afterthought. Rather than be a regurgitation of a research paper in slide format, a presentation is an opportunity for a researcher to explain the context and impact of their work in their own voice; a chance to inspire the audience to want to learn more or dig deeper. Taking inspiration from the
TED conference
, Enigma will have shorter presentations, and the program committee has worked with each speaker to help them craft the best version of their talk.
Hope to see some of you at
USENIX Enigma
later this month!
NIPS 2015 and Machine Learning Research at Google
Sunday, December 06, 2015
Posted by Sanjiv Kumar, Research Scientist
This week, Montreal hosts the
29
th
Annual Conference on Neural Information Processing Systems
(NIPS 2015), 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 2015, with over 140 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 2015, 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 NIPS 2015 in the list below (Googlers highlighted in
blue
).
Google is a Platinum Sponsor of NIPS 2015.
PROGRAM ORGANIZERS
General Chairs
Corinna Cortes
, Neil D. Lawrence
Program Committee includes:
Samy Bengio
,
Gal Chechik
,
Ian Goodfellow
,
Shakir Mohamed
,
Ilya Sutskever
ORAL SESSIONS
Learning Theory and Algorithms for Forecasting Non-stationary Time Series
Vitaly Kuznetsov,
Mehryar Mohri
SPOTLIGHT SESSIONS
Distributed Submodular Cover: Succinctly Summarizing Massive Data
Baharan Mirzasoleiman, Amin Karbasi,
Ashwinkumar Badanidiyuru
, Andreas Krause
Spatial Transformer Networks
Max Jaderberg
,
Karen Simonyan
,
Andrew Zisserman
,
Koray Kavukcuoglu
Pointer Networks
Oriol Vinyals,
Meire Fortunato,
Navdeep Jaitly
Structured Transforms for Small-Footprint Deep Learning
Vikas Sindhwani
,
Tara Sainath
,
Sanjiv Kumar
Spherical Random Features for Polynomial Kernels
Jeffrey Pennington
,
Felix Yu
,
Sanjiv Kumar
POSTERS
Learning to Transduce with Unbounded Memory
Edward Grefenstette
,
Karl Moritz Hermann
,
Mustafa Suleyman,
Phil Blunsom
Deep Knowledge Tracing
Chris Piech, Jonathan Bassen,
Jonathan Huang,
Surya Ganguli, Mehran Sahami, Leonidas Guibas, Jascha Sohl-Dickstein
Hidden Technical Debt in Machine Learning Systems
D Sculley,
Gary Holt
,
Daniel Golovin
,
Eugene Davydov
,
Todd Phillips
,
Dietmar Ebner
,
Vinay Chaudhary
,
Michael Young
,
Jean-Francois Crespo
,
Dan Dennison
Grammar as a Foreign Language
Oriol Vinyals
,
Lukasz Kaiser
,
Terry Koo
,
Slav Petrov
,
Ilya Sutskever
,
Geoffrey Hinton
Stochastic Variational Information Maximisation
Shakir Mohamed
,
Danilo Rezende
Embedding Inference for Structured Multilabel Prediction
Farzaneh Mirzazadeh, Siamak Ravanbakhsh, Bing Xu,
Nan Ding
, Dale Schuurmans
On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators
Changyou Chen,
Nan Ding
, Lawrence Carin
Spectral Norm Regularization of Orthonormal Representations for Graph Transduction
Rakesh Shivanna
, Bibaswan Chatterjee, Raman Sankaran, Chiranjib Bhattacharyya, Francis Bach
Differentially Private Learning of Structured Discrete Distributions
Ilias Diakonikolas,
Moritz Hardt
, Ludwig Schmidt
Nearly Optimal Private LASSO
Kunal Talwar
,
Li Zhang
, Abhradeep Thakurta
Learning Continuous Control Policies by Stochastic Value Gradients
Nicolas Heess
,
Greg Wayne
,
David Silver
,
Timothy Lillicrap
,
Tom Erez
,
Yuval Tassa
Gradient Estimation Using Stochastic Computation Graphs
John Schulman
,
Nicolas Heess
,
Theophane Weber
, Pieter Abbeel
Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
Samy Bengio
,
Oriol Vinyals
,
Navdeep Jaitly
,
Noam Shazeer
Teaching Machines to Read and Comprehend
Karl Moritz Hermann
,
Tomas Kocisky
,
Edward Grefenstette
,
Lasse Espeholt
,
Will Kay
,
Mustafa Suleyman
,
Phil Blunsom
Bayesian dark knowledge
Anoop Korattikara
,
Vivek Rathod
,
Kevin Murphy
, Max Welling
Generalization in Adaptive Data Analysis and Holdout Reuse
Cynthia Dwork, Vitaly Feldman,
Moritz Hardt
, Toniann Pitassi, Omer Reingold, Aaron Roth
Semi-supervised Sequence Learning
Andrew Dai
,
Quoc Le
Natural Neural Networks
Guillaume Desjardins
,
Karen Simonyan
,
Razvan Pascanu
,
Koray Kavukcuoglu
Revenue Optimization against Strategic Buyers
Andres Munoz Medina
,
Mehryar Mohri
WORKSHOPS
Feature Extraction: Modern Questions and Challenges
Workshop Chairs include:
Dmitry Storcheus
,
Afshin Rostamizadeh,
Sanjiv Kumar
Program Committee includes:
Jeffery Pennington
,
Vikas Sindhwani
NIPS Time Series Workshop
Invited Speakers include:
Mehryar Mohri
Panelists include:
Corinna Cortes
Nonparametric Methods for Large Scale Representation Learning
Invited Speakers include:
Amr Ahmed
Machine Learning for Spoken Language Understanding and Interaction
Invited Speakers include:
Larry Heck
Adaptive Data Analysis
Organizers include:
Moritz Hardt
Deep Reinforcement Learning
Organizers include :
David Silver
Invited Speakers include:
Sergey Levine
Advances in Approximate Bayesian Inference
Organizers include :
Shakir Mohamed
Panelists include:
Danilo Rezende
Cognitive Computation: Integrating Neural and Symbolic Approaches
Invited Speakers include:
Ramanathan V. Guha
,
Geoffrey Hinton
,
Greg Wayne
Transfer and Multi-Task Learning: Trends and New Perspectives
Invited Speakers include:
Mehryar Mohri
Poster presentations include:
Andres Munoz Medina
Learning and privacy with incomplete data and weak supervision
Organizers include :
Felix Yu
Program Committee includes:
Alexander Blocker
,
Krzysztof Choromanski
,
Sanjiv Kumar
Speakers include:
Nando de Freitas
Black Box Learning and Inference
Organizers include :
Ali Eslami
Keynotes include:
Geoff Hinton
Quantum Machine Learning
Invited Speakers include:
Hartmut Neven
Bayesian Nonparametrics: The Next Generation
Invited Speakers include:
Amr Ahmed
Bayesian Optimization: Scalability and Flexibility
Organizers include:
Nando de Freitas
Reasoning, Attention, Memory (RAM)
Invited speakers include:
Alex Graves
,
Ilya Sutskever
Extreme Classification 2015: Multi-class and Multi-label Learning in Extremely Large Label Spaces
Panelists include:
Mehryar Mohri
,
Samy Bengio
Invited speakers include:
Samy Bengio
Machine Learning Systems
Invited speakers include:
Jeff Dean
SYMPOSIA
Brains, Mind and Machines
Invited Speakers include:
Geoffrey Hinton
,
Demis Hassabis
Deep Learning Symposium
Program Committee Members include:
Samy Bengio
,
Phil Blunsom
,
Nando De Freitas
,
Ilya Sutskever
,
Andrew Zisserman
Invited Speakers include:
Max Jaderberg
,
Sergey Ioffe
,
Alexander Graves
Algorithms Among Us: The Societal Impacts of Machine Learning
Panelists include:
Shane Legg
TUTORIALS
NIPS 2015 Deep Learning Tutorial
Geoffrey E. Hinton
,
Yoshua Bengio
,
Yann LeCun
Large-Scale Distributed Systems for Training Neural Networks
Jeff Dean
,
Oriol Vinyals
VLDB 2015 and Database Research at Google
Monday, August 31, 2015
Posted by Corinna Cortes, Head of Google Research NY and Cong Yu, Research Scientist
This week, Kohala, Hawaii hosts the
41st International Conference of Very Large Databases
(VLDB 2015), a premier annual international forum for data management and database researchers, vendors, practitioners, application developers and users. As a leader in Database research, Google will have a strong presence at VLDB 2015 with many Googlers publishing work, organizing workshops and presenting demos.
The research Google is presenting at VLDB involves the work of Structured Data teams who are building intelligent and efficient systems to discover, annotate and explore structured data from the Web, surfacing them creatively through Google products (such as
structured snippets
and
table search
), as well as engineering efforts that create scalable, reliable, fast and general-purpose infrastructure for large-scale data processing (such as
F1
,
Mesa
, and Google Cloud's
BigQuery
).
If you are attending VLDB 2015, 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 VLDB 2015 in the list below (Googlers highlighted in
blue
).
Google is a Gold Sponsor of VLDB 2015.
Papers:
Keys for Graphs
Wenfei Fan, Zhe Fan, Chao Tian,
Xin Luna Dong
In-Memory Performance for Big Data
Goetz Graefe, Haris Volos, Hideaki Kimura, Harumi Kuno, Joseph Tucek, Mark Lillibridge,
Alistair Veitch
The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing
Tyler Akidau
,
Robert Bradshaw
,
Craig Chambers
,
Slava Chernyak
,
Rafael Fernández-Moctezuma
,
Reuven Lax
,
Sam McVeety
,
Daniel Mills
,
Frances Perry
,
Eric Schmidt
,
Sam Whittle
Resource Bricolage for Parallel Database Systems
Jiexing Li
, Jeffrey Naughton, Rimma Nehme
AsterixDB: A Scalable, Open Source BDMS
Sattam Alsubaiee, Yasser Altowim, Hotham Altwaijry, Alex Behm, Vinayak Borkar, Yingyi Bu, Michael Carey, Inci Cetindil,
Madhusudan Cheelangi
, Khurram Faraaz, Eugenia Gabrielova, Raman Grover, Zachary Heilbron, Young-Seok Kim, Chen Li, Guangqiang Li, Ji Mahn Ok, Nicola Onose, Pouria Pirzadeh, Vassilis Tsotras, Rares Vernica, Jian Wen, Till Westmann
Knowledge-Based Trust: A Method to Estimate the Trustworthiness of Web Sources
Xin Luna Dong
,
Evgeniy Gabrilovich
,
Kevin Murphy
,
Van Dang
,
Wilko Horn
,
Camillo Lugaresi
,
Shaohua Sun
,
Wei Zhang
Efficient Evaluation of Object-Centric Exploration Queries for Visualization
You Wu,
Boulos Harb
, Jun Yang,
Cong Yu
Interpretable and Informative Explanations of Outcomes
Kareem El Gebaly, Parag Agrawal, Lukasz Golab,
Flip Korn
, Divesh Srivastava
Take me to your leader! Online Optimization of Distributed Storage Configurations
Artyom Sharov,
Alexander Shraer
,
Arif Merchant
,
Murray Stokely
TreeScope: Finding Structural Anomalies In Semi-Structured Data
Shanshan Ying,
Flip Korn
, Barna Saha, Divesh Srivastava
Workshops:
Workshop on Big-Graphs Online Querying - Big-O(Q) 2015
Workshop co-chair:
Cong Yu
3rd International Workshop on In-Memory Data Management and Analytics
Program committee includes:
Sandeep Tata
High-Availability at Massive Scale: Building Google's Data Infrastructure for Ads
Invited talk at BIRTE by:
Ashish Gupta
,
Jeff Shute
Demonstrations:
KATARA: Reliable Data Cleaning with Knowledge Bases and Crowdsourcing
Xu Chu, John Morcos, Ihab Ilyas, Mourad Ouzzani, Paolo Papotti, Nan Tang,
Yin Ye
Error Diagnosis and Data Profiling with Data X-Ray
Xiaolan Wang, Mary Feng, Yue Wang,
Xin Luna Dong
, Alexandra Meliou
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