Tag Archives: conferences

Google at CVPR 2017

Posted by Christian Howard, Editor-in-Chief, Research Communications

From July 21-26, Honolulu, Hawaii hosts the 2017 Conference on Computer Vision and Pattern Recognition (CVPR 2017), the premier annual computer vision event comprising the main conference and several co-located workshops and tutorials. As a leader in computer vision research and a Platinum Sponsor, Google will have a strong presence at CVPR 2017 — over 250 Googlers will be in attendance to present papers and invited talks at the conference, and to organize and participate in multiple workshops.

If you are attending CVPR this year, please stop by our booth and chat with our researchers who are actively pursuing the next generation of intelligent systems that utilize the latest machine learning techniques applied to various areas of machine perception. Our researchers will also be available to talk about and demo several recent efforts, including the technology behind Headset Removal for Virtual and Mixed Reality, Image Compression with Neural Networks, Jump, TensorFlow Object Detection API and much more.

You can learn more about our research being presented at CVPR 2017 in the list below (Googlers highlighted in blue).

Organizing Committee
Corporate Relations Chair - Mei Han
Area Chairs include - Alexander Toshev, Ce Liu, Vittorio Ferrari, David Lowe

Papers
Training object class detectors with click supervision
Dim Papadopoulos, Jasper Uijlings, Frank Keller, Vittorio Ferrari

Unsupervised Pixel-Level Domain Adaptation With Generative Adversarial Networks
Konstantinos Bousmalis, Nathan Silberman, David Dohan, Dumitru Erhan, Dilip Krishnan

BranchOut: Regularization for Online Ensemble Tracking With Convolutional Neural Networks Bohyung Han, Jack Sim, Hartwig Adam

Enhancing Video Summarization via Vision-Language Embedding
Bryan A. Plummer, Matthew Brown, Svetlana Lazebnik

Learning by Association — A Versatile Semi-Supervised Training Method for Neural Networks Philip Haeusser, Alexander Mordvintsev, Daniel Cremers

Context-Aware Captions From Context-Agnostic Supervision
Ramakrishna Vedantam, Samy Bengio, Kevin Murphy, Devi Parikh, Gal Chechik

Spatially Adaptive Computation Time for Residual Networks
Michael Figurnov, Maxwell D. Collins, Yukun Zhu, Li Zhang, Jonathan Huang, Dmitry Vetrov, Ruslan Salakhutdinov

Xception: Deep Learning With Depthwise Separable Convolutions
François Chollet

Deep Metric Learning via Facility Location
Hyun Oh Song, Stefanie Jegelka, Vivek Rathod, Kevin Murphy

Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors
Jonathan Huang, Vivek Rathod, Chen Sun, Menglong Zhu, Anoop Korattikara, Alireza Fathi, Ian Fischer, Zbigniew Wojna, Yang Song, Sergio Guadarrama, Kevin Murphy

Synthesizing Normalized Faces From Facial Identity Features
Forrester Cole, David Belanger, Dilip Krishnan, Aaron Sarna, Inbar Mosseri, William T. Freeman

Towards Accurate Multi-Person Pose Estimation in the Wild
George Papandreou, Tyler Zhu, Nori Kanazawa, Alexander Toshev, Jonathan Tompson, Chris Bregler, Kevin Murphy

GuessWhat?! Visual Object Discovery Through Multi-Modal Dialogue
Harm de Vries, Florian Strub, Sarath Chandar, Olivier Pietquin, Hugo Larochelle, Aaron Courville

Learning discriminative and transformation covariant local feature detectors
Xu Zhang, Felix X. Yu, Svebor Karaman, Shih-Fu Chang

Full Resolution Image Compression With Recurrent Neural Networks
George Toderici, Damien Vincent, Nick Johnston, Sung Jin Hwang, David Minnen, Joel Shor, Michele Covell

Learning From Noisy Large-Scale Datasets With Minimal Supervision
Andreas Veit, Neil Alldrin, Gal Chechik, Ivan Krasin, Abhinav Gupta, Serge Belongie

Unsupervised Learning of Depth and Ego-Motion From Video
Tinghui Zhou, Matthew Brown, Noah Snavely, David G. Lowe

Cognitive Mapping and Planning for Visual Navigation
Saurabh Gupta, James Davidson, Sergey Levine, Rahul Sukthankar, Jitendra Malik

Fast Fourier Color Constancy
Jonathan T. Barron, Yun-Ta Tsai

On the Effectiveness of Visible Watermarks
Tali Dekel, Michael Rubinstein, Ce Liu, William T. Freeman

YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video
Esteban Real, Jonathon Shlens, Stefano Mazzocchi, Xin Pan, Vincent Vanhoucke

Workshops
Deep Learning for Robotic Vision
Organizers include: Anelia Angelova, Kevin Murphy
Program Committee includes: George Papandreou, Nathan Silberman, Pierre Sermanet

The Fourth Workshop on Fine-Grained Visual Categorization
Organizers include: Yang Song
Advisory Panel includes: Hartwig Adam
Program Committee includes: Anelia Angelova, Yuning Chai, Nathan Frey, Jonathan Krause, Catherine Wah, Weijun Wang

Language and Vision Workshop
Organizers include: R. Sukthankar

The First Workshop on Negative Results in Computer Vision
Organizers include: R. Sukthankar, W. Freeman, J. Malik

Visual Understanding by Learning from Web Data
General Chairs include: Jesse Berent, Abhinav Gupta, Rahul Sukthankar
Program Chairs include: Wei Li

YouTube-8M Large-Scale Video Understanding Challenge
General Chairs: Paul Natsev, Rahul Sukthankar
Program Chairs: Joonseok Lee, George Toderici
Challenge Organizers: Sami Abu-El-Haija, Anja Hauth, Nisarg Kothari, Hanhan Li, Sobhan Naderi Parizi, Balakrishnan Varadarajan, Sudheendra Vijayanarasimhan, Jian Wang

Research at Google and ICLR 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

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

NIPS 2016 & Research at Google

Posted by Doug Eck, Research Scientist, Google Brain Team

This week, Barcelona hosts the 30th 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

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

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

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.

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 sveral recent efforts, including the technology behind Motion Stills and a live demo of neural network-based image compression. 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

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, Greg Wayne, Benigno Uria, Nal Kalchbrenner, Alex Graves

 Asynchronous Methods for Deep Reinforcement Learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu

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, Nando de Freitas, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot

Exploiting Cyclic Symmetry in Convolutional Neural Networks
Sander Dieleman, Jeffrey De Fauw, Koray Kavukcuoglu

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, Ilya Sutskever, Sergey Levine

Meta-Learning with Memory-Augmented Neural Networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy Lillicrap

One-Shot Generalization in Deep Generative Models
Danilo Rezende, Shakir Mohamed, Daan Wierstra

Pixel Recurrent Neural Networks (Best Paper Award recipient)
Aaron Van den Oord, Nal Kalchbrenner, Koray Kavukcuoglu

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, Danilo Rezende

Workshops
Abstraction in Reinforcement Learning
Organizing Committee: Daniel Mankowitz, Timothy Mann, Shie Mannor
Invited Speaker: David Silver

Deep Learning Workshop
Organizers: Antoine Bordes, Kyunghyun Cho, Emily Denton, Nando de Freitas, Rob Fergus
Invited Speaker: Raia Hadsell

Neural Networks Back To The Future
Organizers: Léon Bottou, David Grangier, Tomas Mikolov, John Platt

Data-Efficient Machine Learning
Organizers: Marc Deisenroth, Shakir Mohamed, 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

 Rigorous Data Dredging: Theory and Tools for Adaptive Data Analysis
Moritz Hardt, Aaron Roth

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

Lessons learned while protecting Gmail

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:
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?

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.
  • 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

Posted by Sanjiv Kumar, Research Scientist

This week, Montreal hosts the 29th 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

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