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Google at NeurIPS 2018



This week, Montréal hosts the 32nd annual Conference on Neural Information Processing Systems (NeurIPS 2018), the biggest machine learning conference of the year. The conference includes invited talks, demonstrations and presentations of some of the latest in machine learning research. Google will have a strong presence at NeurIPS 2018, with more than 400 Googlers attending in order to contribute to, and learn from, the broader academic research community via talks, posters, workshops, competitions and tutorials. We will be presenting work that pushes the boundaries of what is possible in language understanding, translation, speech recognition and visual & audio perception, with Googlers co-authoring nearly 100 accepted papers (see below).

At the forefront of machine learning, Google is actively exploring virtually all aspects of the field spanning both theory and applications. This research is often inspired by real product needs but increasingly more often driven by scientific curiosity. Given the range of research projects that we pursue, we have found it useful to define a new framework which helps crystalize the goals of projects and allows us to measure progress and success in appropriate ways. Our contributions to NeurIPS and to the broader research community in general are integral to our research mission.

If you are attending NeurIPS 2018, we hope you’ll stop by our booth and chat with our researchers about the projects and opportunities at Google that go into solving the world's most challenging research problems, and to see demonstrations of some of the exciting research we pursue. You can also learn more about our work being presented in the list below (Googlers highlighted in blue).

Google is a Platinum Sponsor of NeurIPS 2018.

NeurIPS Foundation Board
Corinna Cortes, John C. Platt, Fernando Pereira

NeurIPS Organizing Committee
General Chair: Samy Bengio
Program Co-Chair: Hugo Larochelle
Party Chair: Douglas Eck
Diversity and Inclusion Co-Chair: Katherine A. Heller

NeurIPS Program Committee
Senior Area Chairs include:Angela Yu, Claudio Gentile, Cordelia Schmid, Corinna Cortes, Csaba Szepesvari, Dale Schuurmans, Elad Hazan, Mehryar Mohri, Raia Hadsell, Satyen Kale, Yishay Mansour, Afshin Rostamizadeh, Alex Kulesza

Area Chairs include: Amin Karbasi, Amir Globerson, Amit Daniely, Andras Gyorgy, Andriy Mnih, Been Kim, Branislav Kveton, Ce Liu, D Sculley, Danilo Rezende, Danny TarlowDavid Balduzzi, Denny Zhou, Dilan Gorur, Dumitru Erhan, George Dahl, Graham Taylor, Ian Goodfellow, Jasper Snoek, Jean-Philippe Vert, Jia Deng, Jon Shlens, Karen Simonyan, Kevin Swersky, Kun Zhang, Lihong Li, Marc G. Bellemare, Marco Cuturi, Maya Gupta, Michael BowlingMichalis Titsias, Mohammad Norouzi, Mouhamadou Moustapha Cisse, Nicolas Le Roux, Remi Munos, Sanjiv Kumar, Sanmi Koyejo, Sergey Levine, Silvia Chiappa, Slav PetrovSurya Ganguli, Timnit Gebru, Timothy Lillicrap, Viren Jain, Vitaly Feldman, Vitaly Kuznetsov

Workshops Program Committee includes: Mehryar Mohri, Sergey Levine

Accepted Papers
3D-Aware Scene Manipulation via Inverse Graphics
Shunyu Yao, Tzu Ming Harry Hsu, Jun-Yan Zhu, Jiajun Wu, Antonio Torralba, William T. Freeman, Joshua B. Tenenbaum

A Retrieve-and-Edit Framework for Predicting Structured Outputs
Tatsunori Hashimoto, Kelvin Guu, Yonatan Oren, Percy Liang

Adversarial Attacks on Stochastic Bandits
Kwang-Sung Jun, Lihong Li, Yuzhe Ma, Xiaojin Zhu

Adversarial Examples that Fool both Computer Vision and Time-Limited Humans
Gamaleldin F. Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alex Kurakin, Ian Goodfellow, Jascha Sohl-Dickstein

Adversarially Robust Generalization Requires More Data
Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Madry

Are GANs Created Equal? A Large-Scale Study
Mario Lucic, Karol Kurach, Marcin Michalski, Olivier Bousquet, Sylvain Gelly

Collaborative Learning for Deep Neural Networks
Guocong Song, Wei Chai

Completing State Representations using Spectral Learning
Nan Jiang, Alex Kulesza, Santinder Singh

Content Preserving Text Generation with Attribute Controls
Lajanugen Logeswaran, Honglak Lee, Samy Bengio

Context-aware Synthesis and Placement of Object Instances
Donghoon Lee, Sifei Liu, Jinwei Gu, Ming-Yu Liu, Ming-Hsuan Yang, Jan Kautz

Co-regularized Alignment for Unsupervised Domain Adaptation
Abhishek Kumar, Prasanna Sattigeri, Kahini Wadhawan, Leonid Karlinsky, Rogerlo Feris, William T. Freeman, Gregory Wornell

cpSGD: Communication-efficient and differentially-private distributed SGD
Naman Agarwal, Ananda Theertha Suresh, Felix Yu, Sanjiv Kumar, H. Brendan Mcmahan

Data Center Cooling Using Model-Predictive Control
Nevena Lazic, Craig Boutilier, Tyler Lu, Eehern Wong, Binz Roy, MK Ryu, Greg Imwalle

Data-Efficient Hierarchical Reinforcement Learning
Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine

Deep Attentive Tracking via Reciprocative Learning
Shi Pu, Yibing Song, Chao Ma, Honggang Zhang, Ming-Hsuan Yang

Generalizing Point Embeddings Using the Wasserstein Space of Elliptical Distributions
Boris Muzellec, Marco Cuturi

GLoMo: Unsupervised Learning of Transferable Relational Graphs
Zhilin Yang, Jake (Junbo) Zhao, Bhuwan Dhingra, Kaiming He, William W. Cohen, Ruslan Salakhutdinov, Yann LeCun

GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking
Patrick Chen, Si Si, Yang Li, Ciprian Chelba, Cho-Jui Hsieh

Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections
Xin Zhang, Armando Solar-Lezama, Rishabh Singh

Learning Hierarchical Semantic Image Manipulation through Structured Representations
Seunghoon Hong, Xinchen Yan, Thomas Huang, Honglak Lee

Learning Temporal Point Processes via Reinforcement Learning
Shuang Li, Shuai Xiao, Shixiang Zhu, Nan Du, Yao Xie, Le Song

Learning Towards Minimum Hyperspherical Energy
Weiyang Liu, Rongmei Lin, Zhen Liu, Lixin Liu, Zhiding Yu, Bo Dai, Le Song

Mesh-TensorFlow: Deep Learning for Supercomputers
Noam Shazeer, Youlong Cheng, Niki Parmar, Dustin Tran, Ashish Vaswani, Penporn Koanantakool, Peter Hawkins, HyoukJoong Lee, Mingsheng Hong, Cliff Young, Ryan Sepassi, Blake Hechtman

MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare
Edward Choi, Cao Xiao, Walter F. Stewart, Jimeng Sun

Searching for Efficient Multi-Scale Architectures for Dense Image Prediction
Liang-Chieh Chen, Maxwell D. Collins, Yukun Zhu, George Papandreou, Barret Zoph, Florian Schroff, Hartwig Adam, Jonathon Shlens

SplineNets: Continuous Neural Decision Graphs
Cem Keskin, Shahram Izadi

Task-Driven Convolutional Recurrent Models of the Visual System
Aran Nayebi, Daniel Bear, Jonas Kubilius, Kohitij Kar, Surya Ganguli, David Sussillo, James J. DiCarlo, Daniel L. K. Yamins

To Trust or Not to Trust a Classifier
Heinrich Jiang, Been Kim, Melody Guan, Maya Gupta

Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
Ye Jia, Yu Zhang, Ron J. Weiss, Quan Wang, Jonathan Shen, Fei Ren, Zhifeng Chen, Patrick Nguyen, Ruoming Pang, Ignacio Lopez Moreno, Yonghui Wu

Algorithms and Theory for Multiple-Source Adaptation
Judy Hoffman, Mehryar Mohri, Ningshan Zhang

A Lyapunov-based Approach to Safe Reinforcement Learning
Yinlam Chow, Ofir Nachum, Edgar Duenez-Guzman, Mohammad Ghavamzadeh

Adaptive Methods for Nonconvex Optimization
Manzil Zaheer, Sashank Reddi, Devendra Sachan, Satyen Kale, Sanjiv Kumar

Assessing Generative Models via Precision and Recall
Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly

A Loss Framework for Calibrated Anomaly Detection
Aditya Menon, Robert Williamson

Blockwise Parallel Decoding for Deep Autoregressive Models
Mitchell Stern, Noam Shazeer, Jakob Uszkoreit

Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation
Qiang Liu, Lihong Li, Ziyang Tang, Dengyong Zhou

Contextual Pricing for Lipschitz Buyers
Jieming Mao, Renato Leme, Jon Schneider

Coupled Variational Bayes via Optimization Embedding
Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song

Data Amplification: A Unified and Competitive Approach to Property Estimation
Yi HAO, Alon Orlitsky, Ananda Theertha Suresh, Yihong Wu

Deep Network for the Integrated 3D Sensing of Multiple People in Natural Images
Elisabeta Marinoiu, Mihai Zanfir, Alin-Ionut Popa, Cristian Sminchisescu

Deep Non-Blind Deconvolution via Generalized Low-Rank Approximation
Wenqi Ren, Jiawei Zhang, Lin Ma, Jinshan Pan, Xiaochun Cao, Wei Liu, Ming-Hsuan Yang

Diminishing Returns Shape Constraints for Interpretability and Regularization
Maya Gupta, Dara Bahri, Andrew Cotter, Kevin Canini

DropBlock: A Regularization Method for Convolutional Networks
Golnaz Ghiasi, Tsung-Yi Lin, Quoc V. Le

Generalization Bounds for Uniformly Stable Algorithms
Vitaly Feldman, Jan Vondrak

Geometrically Coupled Monte Carlo Sampling
Mark Rowland, Krzysztof Choromanski, Francois Chalus, Aldo Pacchiano, Tamas Sarlos, Richard E. Turner, Adrian Weller

GILBO: One Metric to Measure Them All
Alexander A. Alemi, Ian Fischer

Insights on Representational Similarity in Neural Networks with Canonical Correlation
Ari S. Morcos, Maithra Raghu, Samy Bengio

Improving Online Algorithms via ML Predictions
Manish Purohit, Zoya Svitkina, Ravi Kumar

Learning to Exploit Stability for 3D Scene Parsing
Yilun Du, Zhijan Liu, Hector Basevi, Ales Leonardis, William T. Freeman, Josh Tenembaum, Jiajun Wu

Maximizing Induced Cardinality Under a Determinantal Point Process
Jennifer Gillenwater, Alex Kulesza, Sergei Vassilvitskii, Zelda Mariet

Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing
Chen Liang, Mohammad Norouzi, Jonathan Berant, Quoc V. Le, Ni Lao

PCA of High Dimensional Random Walks with Comparison to Neural Network Training
Joseph M. Antognini, Jascha Sohl-Dickstein

Predictive Approximate Bayesian Computation via Saddle Points
Yingxiang Yang, Bo Dai, Negar Kiyavash, Niao He

Recurrent World Models Facilitate Policy Evolution
David Ha, Jürgen Schmidhuber

Sanity Checks for Saliency Maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, Been Kim

Simple, Distributed, and Accelerated Probabilistic Programming
Dustin Tran, Matthew Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan, Alexey Radul, Matthew Johnson, Rif A. Saurous

Tangent: Automatic Differentiation Using Source-Code Transformation for Dynamically Typed Array Programming
Bart van Merriënboer, Dan Moldovan, Alex Wiltschko

The Emergence of Multiple Retinal Cell Types Through Efficient Coding of Natural Movies
Samuel A. Ocko, Jack Lindsey, Surya Ganguli, Stephane Deny

The Everlasting Database: Statistical Validity at a Fair Price
Blake Woodworth, Vitaly Feldman, Saharon Rosset, Nathan Srebro

The Spectrum of the Fisher Information Matrix of a Single-Hidden-Layer Neural Network
Jeffrey Pennington, Pratik Worah

A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
Kimin Lee, Kibok Lee, Honglak Lee, Jinwoo Shin

Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language
Matthew D. Hoffman, Matthew Johnson, Dustin Tran

A Bayesian Nonparametric View on Count-Min Sketch
Diana Cai, Michael Mitzenmacher, Ryan Adams (no longer at Google)

Automatic Differentiation in ML: Where We are and Where We Should be Going
Bart van Merriënboer, Olivier Breuleux, Arnaud Bergeron, Pascal Lamblin

Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures
Sergey Bartunov, Adam Santoro, Blake A. Richards, Geoffrey E. Hinton, Timothy P. Lillicrap

Deep Generative Models for Distribution-Preserving Lossy Compression
Michael Tschannen, Eirikur Agustsson, Mario Lucic

Deep Structured Prediction with Nonlinear Output Transformations
Colin Graber, Ofer Meshi, Alexander Schwing

Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning
Supasorn Suwajanakorn, Noah Snavely, Jonathan Tompson, Mohammad Norouzi

Transfer Learning with Neural AutoML
Catherine Wong, Neil Houlsby, Yifeng Lu, Andrea Gesmundo

Efficient Gradient Computation for Structured Output Learning with Rational and Tropical Losses
Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Dmitry Storcheus, Scott Yang

Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification
Harsh Shrivastava, Eugene Bart, Bob Price, Hanjun Dai, Bo Dai, Srinivas Aluru

Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization
Blake Woodworth, Jialei Wang, Brendan McMahan, Nathan Srebro

Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies
Sungryull Sohn, Junhyuk Oh, Honglak Lee

Human-in-the-Loop Interpretability Prior
Isaac Lage, Andrew Slavin Ross, Been Kim, Samuel J. Gershman, Finale Doshi-Velez

Joint Autoregressive and Hierarchical Priors for Learned Image Compression
David Minnen, Johannes Ballé, George D Toderici

Large-Scale Computation of Means and Clusters for Persistence Diagrams Using Optimal Transport
Théo Lacombe, Steve Oudot, Marco Cuturi

Learning to Reconstruct Shapes from Unseen Classes
Xiuming Zhang, Zhoutong Zhang, Chengkai Zhang, Joshua B. Tenenbaum, William T. Freeman, Jiajun Wu

Large Margin Deep Networks for Classification
Gamaleldin Fathy Elsayed, Dilip Krishnan, Hossein Mobahi, Kevin Regan, Samy Bengio

Mallows Models for Top-k Lists
Flavio Chierichetti, Anirban Dasgupta, Shahrzad Haddadan, Ravi Kumar, Silvio Lattanzi

Meta-Learning MCMC Proposals
Tongzhou Wang, YI WU, Dave Moore, Stuart Russell

Non-delusional Q-Learning and Value-Iteration
Tyler Lu, Dale Schuurmans, Craig Boutilier

Online Learning of Quantum States
Scott Aaronson, Xinyi Chen, Elad Hazan, Satyen Kale, Ashwin Nayak

Online Reciprocal Recommendation with Theoretical Performance Guarantees
Fabio Vitale, Nikos Parotsidis, Claudio Gentile

Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization
Rad Niazadeh, Tim Roughgarden, Joshua R. Wang

Policy Regret in Repeated Games
Raman Arora, Michael Dinitz, Teodor Vanislavov Marinov, Mehryar Mohri

Provable Variational Inference for Constrained Log-Submodular Models
Josip Djolonga, Stefanie Jegelka, Andreas Krause

Realistic Evaluation of Deep Semi-Supervised Learning Algorithms
Avital Oliver, Augustus Odena, Colin Raffel, Ekin D. Cubuk, Ian J. Goodfellow

Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion
Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, Honglak Lee

Visual Object Networks: Image Generation with Disentangled 3D Representations
JunYan Zhu, Zhoutong Zhang, Chengkai Zhang, Jiajun Wu, Antonio Torralba, Josh Tenenbaum, William T. Freeman

Watch Your Step: Learning Node Embeddings via Graph Attention
Sami Abu-El-Haija, Bryan Perozzi, Rami AlRfou, Alexander Alemi

Workshops
2nd Workshop on Machine Learning on the Phone and Other Consumer Devices
Co-Chairs include: Sujith Ravi, Wei Chai, Hrishikesh Aradhye

Bayesian Deep Learning
Workshop Organizers include: Kevin Murphy

Continual Learning
Workshop Organizers include: Marc Pickett

The Second Conversational AI Workshop – Today's Practice and Tomorrow's Potential
Workshop Organizers include: Dilek Hakkani-Tur

Visually Grounded Interaction and Language
Workshop Organizers include: Olivier Pietquin

Workshop on Ethical, Social and Governance Issues in AI
Workshop Organizers include: D. Sculley

AI for Social Good
Workshop Program Committee includes: Samuel Greydanus

Black in AI
Workshop Organizers: Mouhamadou Moustapha Cisse, Timnit Gebru
Program Committee: Irwan Bello, Samy Bengio, Ian Goodfellow, Hugo Larochelle, Margaret Mitchell

Interpretability and Robustness in Audio, Speech, and Language
Workshop Organizers include: Ehsan Variani, Bhuvana Ramabhadran

LatinX in AI
Workshop Organizers includes: Pablo Samuel Castro
Program Committee includes: Sergio Guadarrama

Machine Learning for Systems
Workshop Organizers include: Anna Goldie, Azalia Mirhoseini, Kevin Swersky, Milad Hashemi
Program Committee includes: Simon Kornblith, Nicholas Frosst, Amir Yazdanbakhsh, Azade Nazi, James Bradbury, Sharan Narang, Martin Maas, Carlos Villavieja

Queer in AI
Workshop Organizers include: Raphael Gontijo Lopes

Second Workshop on Machine Learning for Creativity and Design
Workshop Organizers include: Jesse Engel, Adam Roberts

Workshop on Security in Machine Learning
Workshop Organizers include: Nicolas Papernot

Tutorial
Visualization for Machine Learning
Fernanda Viégas, Martin Wattenberg

Source: Google AI Blog


Google at EMNLP 2018



This week, the annual conference on Empirical Methods in Natural Language Processing (EMNLP 2018) will be held in Brussels, Belgium. Google will have a strong presence at EMNLP with several of our researchers presenting their research on a diverse set of topics, including language identification, segmentation, semantic parsing and question answering, additionally serving in various levels of organization in the conference. Googlers will also be presenting their papers and participating in the co-located Conference on Computational Natural Language Learning (CoNLL 2018) shared task on multilingual parsing.

In addition to this involvement, we are sharing several new datasets with the academic community that are released with papers published at EMNLP, with the goal of accelerating progress in empirical natural language processing (NLP). These releases are designed to help account for mismatches between the datasets a machine learning model is trained and tested on, and the inputs an NLP system would be asked to handle “in the wild”. All of the datasets we are releasing include realistic, naturally occurring text, and fall into two main categories: 1) challenge sets for well-studied core NLP tasks (part-of-speech tagging, coreference) and 2) datasets to encourage new directions of research on meaning preservation under rephrasings/edits (query well-formedness, split-and-rephrase, atomic edits):
  • Noun-Verb Ambiguity in POS Tagging Dataset: English part-of-speech taggers regularly make egregious errors related to noun-verb ambiguity, despite high accuracies on standard datasets. For example: in “Mark which area you want to distress” several state-of-the-art taggers annotate “Mark” as a noun instead of a verb. We release a new dataset of over 30,000 naturally occurring non-trivial annotated examples of noun-verb ambiguity. Taggers previously indistinguishable from each other have accuracies ranging from 57% to 75% accuracy on this challenge set.
  • Query Wellformedness Dataset: Web search queries are usually “word-salad” style queries with little resemblance to natural language questions (“barack obama height” as opposed to “What is the height of Barack Obama?”). Differentiating a natural language question from a query is of importance to several applications include dialogue. We annotate and release 25,100 queries from the open-source Paralex corpus with ratings on how close they are to well-formed natural language questions.
  • WikiSplit: Split and Rephrase Dataset Extracted from Wikipedia Edits: We extract examples of sentence splits from Wikipedia edits where one sentence gets split into two sentences that together preserve the original meaning of the sentence (E.g. “Street Rod is the first in a series of two games released for the PC and Commodore 64 in 1989.” is split into “Street Rod is the first in a series of two games.” and “It was released for the PC and Commodore 64 in 1989.”) The released corpus contains one million sentence splits with a vocabulary of more than 600,000 words. 
  • WikiAtomicEdits: A Multilingual Corpus of Atomic Wikipedia Edits: Information about how people edit language in Wikipedia can be used to understand the structure of language itself. We pay particular attention to two atomic edits: insertions and deletions that consist of a single contiguous span of text. We extract around 43 million such edits in 8 languages and show that they provide valuable information about entailment and discourse. For example, insertion of “in 1949” adds a prepositional phrase to the sentence “She died there after a long illness” resulting in “She died there in 1949 after a long illness”.
These datasets join the others that Google has recently released, such as Conceptual Captions and GAP Coreference Resolution in addition to our past contributions.

Below is a full list of Google’s involvement and publications being presented at EMNLP and CoNLL (Googlers highlighted in blue). We are particularly happy to announce that the paper “Linguistically-Informed Self-Attention for Semantic Role Labeling” was awarded one of the two Best Long Paper awards. This work was done by our 2017 intern Emma Strubell, Googlers Daniel Andor, David Weiss and Google Faculty Advisor Andrew McCallum. We congratulate these authors, and all other researchers who are presenting their work at the conference.

Area Chairs Include:
Ming-Wei Chang, Marius Pasca, Slav Petrov, Emily Pitler, Meg Mitchell, Taro Watanabe

EMNLP Publications
A Challenge Set and Methods for Noun-Verb Ambiguity
Ali Elkahky, Kellie Webster, Daniel Andor, Emily Pitler

A Fast, Compact, Accurate Model for Language Identification of Codemixed Text
Yuan Zhang, Jason Riesa, Daniel Gillick, Anton Bakalov, Jason Baldridge, David Weiss

AirDialogue: An Environment for Goal-Oriented Dialogue Research
Wei Wei, Quoc Le, Andrew Dai, Jia Li

Content Explorer: Recommending Novel Entities for a Document Writer
Michal Lukasik, Richard Zens

Deep Relevance Ranking using Enhanced Document-Query Interactions
Ryan McDonald, George Brokos, Ion Androutsopoulos

HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, Christopher D. Manning

Identifying Well-formed Natural Language Questions
Manaal Faruqui, Dipanjan Das

Learning To Split and Rephrase From Wikipedia Edit History
Jan A. Botha, Manaal Faruqui, John Alex, Jason Baldridge, Dipanjan Das

Linguistically-Informed Self-Attention for Semantic Role Labeling
Emma Strubell, Patrick Verga, Daniel Andor, David Weiss, Andrew McCallum

Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text
Haitian Sun, Bhuwan Dhingra, Manzil Zaheer, Kathryn Mazaitis, Ruslan Salakhutdinov, William Cohen

Noise Contrastive Estimation for Conditional Models: Consistency and Statistical Efficiency
Zhuang Ma, Michael Collins

Part-of-Speech Tagging for Code-Switched, Transliterated Texts without Explicit Language Identification
Kelsey Ball, Dan Garrette

Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension
Minjoon Seo, Tom Kwiatkowski, Ankur P. Parikh, Ali Farhadi, Hannaneh Hajishirzi

Policy Shaping and Generalized Update Equations for Semantic Parsing from Denotations
Dipendra Misra, Ming-Wei Chang, Xiaodong He, Wen-tau Yih

Revisiting Character-Based Neural Machine Translation with Capacity and Compression
Colin Cherry, George Foster, Ankur Bapna, Orhan Firat, Wolfgang Macherey

Self-governing neural networks for on-device short text classification
Sujith Ravi, Zornitsa Kozareva

Semi-Supervised Sequence Modeling with Cross-View Training
Kevin Clark, Minh-Thang Luong, Christopher D. Manning, Quoc Le

State-of-the-art Chinese Word Segmentation with Bi-LSTMs
Ji Ma, Kuzman Ganchev, David Weiss

Subgoal Discovery for Hierarchical Dialogue Policy Learning
Da Tang, Xiujun Li, Jianfeng Gao, Chong Wang, Lihong Li, Tony Jebara

SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translation
Xinyi Wang, Hieu Pham, Zihang Dai, Graham Neubig

The Importance of Generation Order in Language Modeling
Nicolas Ford, Daniel Duckworth, Mohammad Norouzi, George Dahl

Training Deeper Neural Machine Translation Models with Transparent Attention
Ankur Bapna, Mia Chen, Orhan Firat, Yuan Cao, Yonghui Wu

Understanding Back-Translation at Scale
Sergey Edunov, Myle Ott, Michael Auli, David Grangier

Unsupervised Natural Language Generation with Denoising Autoencoders
Markus Freitag, Scott Roy

WikiAtomicEdits: A Multilingual Corpus of Wikipedia Edits for Modeling Language and Discourse
Manaal Faruqui, Ellie Pavlick, Ian Tenney, Dipanjan Das

WikiConv: A Corpus of the Complete Conversational History of a Large Online Collaborative Community
Yiqing Hua, Cristian Danescu-Niculescu-Mizil, Dario Taraborelli, Nithum Thain, Jeffery Sorensen, Lucas Dixon

EMNLP Demos
SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing
Taku Kudo, John Richardson

Universal Sentence Encoder for English
Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St. John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, Brian Strope, Ray Kurzweil

CoNLL Shared Task
Multilingual Parsing from Raw Text to Universal Dependencies
Slav Petrov, co-organizer

Universal Dependency Parsing with Multi-Treebank Models
Aaron Smith, Bernd Bohnet, Miryam de Lhoneux, Joakim Nivre, Yan Shao, Sara Stymne
(Winner of the Universal POS Tagging and Morphological Tagging subtasks, using the open-sourced Meta-BiLSTM tagger)

CoNLL Publication
Sentence-Level Fluency Evaluation: References Help, But Can Be Spared!
Katharina Kann, Sascha Rothe, Katja Filippova

Source: Google AI Blog


Googlers on the road: CLS and OSCON 2018

Next week a veritable who’s who of free and open source software luminaries, maintainers and developers will gather to celebrate the 20th annual OSCON and the 20th anniversary of the Open Source Definition. Naturally, the Google Open Source and Google Cloud teams will be there too!

Program chairs at OSCON 2017, left to right:
Rachel Roumeliotis, Kelsey Hightower, Scott Hanselman.
Photo used with permission from O'Reilly Media.
This year OSCON returns to Portland, Oregon and runs from July 16-19. As usual, it is preceded by the free-to-attend Community Leadership Summit on July 14-15.

If you’re curious about our outreach programs, our approach to open source, or any of the open source projects we’ve released, please find us! We’re eager to chat. You’ll find us and many other Googlers throughout the week on stage, in the expo hall, and at several special events that we’re running, including:
Here’s a rundown of the sessions we’re hosting this year:

Sunday, July 15th (Community Leadership Summit)

11:45am   Asking for time and/or money by Cat Allman

Monday, July 16th (Tutorials)

9:00am    Getting started with TensorFlow by Josh Gordon
1:30pm    Introduction to natural language processing with Python by Barbara Fusinska

Tuesday, July 17th (Tutorials)

9:00am    Istio Day opening remarks by Kelsey Hightower
9:00am    TensorFlow Day opening remarks by Edd Wilder-James
9:05am    Sailing to 1.0: Istio community update by April Nassi
9:05am    The state of TensorFlow by Sandeep Gupta
9:30am    Introduction to fairness in machine learning by Hallie Benjamin
9:55am    Farm to table: A TensorFlow story by Gunhan Gulsoy
11:00am  Hassle-free, scalable machine learning with Kubeflow by Barbara Fusinska
11:05am  Istio: Zero-trust communication security for production services by Samrat Ray, Tao Li, and Mak Ahmad
12:00pm  Project Magenta: Machine learning for music and art by Sherol Chen
1:35pm    Istio à la carte by Daniel Ciruli

Wednesday, July 18th (Sessions)

9:00am    Wednesday opening welcome by Kelsey Hightower
11:50am  Machine learning for continuous integration by Joseph Gregorio
1:45pm    Live-coding a beautiful, performant mobile app from scratch by Emily Fortuna and Matt Sullivan
2:35pm    Powering TensorFlow with big data using Apache Beam, Flink, and Spark by Holden Karau
5:25pm    Teaching the Next Generation to FLOSS by Josh Simmons

Thursday, July 19th (Sessions)

9:00am    Thursday opening welcome by Kelsey Hightower
9:40am    20 years later, open source is as important as ever by Sarah Novotny
11:50am  Google’s approach to distributed systems observability by Jaana B. Dogan
2:35pm    gRPC versus REST: Let the battle begin with Alex Borysov
5:05pm    Shenzhen Go: A visual Go environment for everybody, even professionals by Josh Deprez

We look forward to seeing you and the rest of the community there!

By Josh Simmons, Google Open Source

Google at ICML 2018



Machine learning is a key strategic focus at Google, with highly active groups pursuing research in virtually all aspects of the field, including deep learning and more classical algorithms, exploring theory as well as application. We utilize scalable tools and architectures to build machine learning systems that enable us to solve deep scientific and engineering challenges in areas of language, speech, translation, music, visual processing and more.

As a leader in machine learning research, Google is proud to be a Platinum Sponsor of the thirty-fifth International Conference on Machine Learning (ICML 2018), a premier annual event supported by the International Machine Learning Society taking place this week in Stockholm, Sweden. With over 130 Googlers attending the conference to present publications and host workshops, we look forward to our continued collaboration with the larger ML research community.

If you're attending ICML 2018, 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 some of the field's most interesting challenges. Our researchers will also be available to talk about TensorFlow Hub, the latest work from the Magenta project, a Q&A session on the Google AI Residency program and much more. You can also learn more about our research being presented at ICML 2018 in the list below (Googlers highlighted in blue).

ICML 2018 Committees
Board Members include: Andrew McCallumCorinna CortesHugo LarochelleWilliam Cohen
Sponsorship Co-Chair: Ryan Adams

Accepted Publications
Predict and Constrain: Modeling Cardinality in Deep Structured Prediction
Nataly Brukhim, Amir Globerson

Quickshift++: Provably Good Initializations for Sample-Based Mean Shift
Heinrich Jiang, Jennifer Jang, Samory Kpotufe

Learning a Mixture of Two Multinomial Logits
Flavio Chierichetti, Ravi KumarAndrew Tomkins

Structured Evolution with Compact Architectures for Scalable Policy Optimization
Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard E Turner, Adrian Weller

Fixing a Broken ELBO
Alexander Alemi, Ben Poole, Ian FischerJoshua DillonRif SaurousKevin Murphy

Hierarchical Long-term Video Prediction without Supervision
Nevan Wichers, Ruben Villegas, Dumitru ErhanHonglak Lee

Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings
John Co-Reyes, Yu Xuan Liu, Abhishek Gupta, Benjamin Eysenbach, Pieter Abbeel, Sergey Levine

Well Tempered Lasso
Yuanzhi Li, Yoram Singer

Programmatically Interpretable Reinforcement Learning
Abhinav Verma, Vijayaraghavan Murali, Rishabh Singh, Pushmeet Kohli, Swarat Chaudhuri

Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks
Lechao XiaoYasaman BahriJascha Sohl-DicksteinSamuel SchoenholzJeffrey Pennington

On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization
Sanjeev Arora, Nadav Cohen, Elad Hazan

Scalable Deletion-Robust Submodular Maximization: Data Summarization with Privacy and Fairness Constraints
Ehsan Kazemi, Morteza Zadimoghaddam, Amin Karbasi

Data Summarization at Scale: A Two-Stage Submodular Approach
Marko Mitrovic, Ehsan Kazemi, Morteza Zadimoghaddam, Amin Karbasi

Machine Theory of Mind
Neil Rabinowitz, Frank Perbet, Francis Song, Chiyuan Zhang, S. M. Ali Eslami, Matthew Botvinick

Learning to Optimize Combinatorial Functions
Nir Rosenfeld, Eric Balkanski, Amir Globerson, Yaron Singer

Proportional Allocation: Simple, Distributed, and Diverse Matching with High Entropy
Shipra Agarwal, Morteza ZadimoghaddamVahab Mirrokni

Path Consistency Learning in Tsallis Entropy Regularized MDPs
Yinlam Chow, Ofir NachumMohammad Ghavamzadeh

Efficient Neural Architecture Search via Parameters Sharing
Hieu Pham, Melody Guan, Barret ZophQuoc LeJeff Dean

Adafactor: Adaptive Learning Rates with Sublinear Memory Cost
Noam Shazeer, Mitchell Stern

Learning Memory Access Patterns
Milad HashemiKevin SwerskyJamie Smith, Grant Ayers, Heiner Litz, Jichuan Chang, Christos Kozyrakis, Parthasarathy Ranganathan

SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation
Bo Dai, Albert Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, Le Song

Scalable Bilinear Pi Learning Using State and Action Features
Yichen Chen, Lihong Li, Mengdi Wang

Distributed Asynchronous Optimization with Unbounded Delays: How Slow Can You Go?
Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos, Peter Glynn, Yinyu Ye, Li-Jia Li, Li Fei-Fei

Shampoo: Preconditioned Stochastic Tensor Optimization
Vineet Gupta, Tomer Koren, Yoram Singer

Parallel and Streaming Algorithms for K-Core Decomposition
Hossein Esfandiari, Silvio LattanziVahab Mirrokni

Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?
Maithra RaghuAlexander Irpan, Jacob Andreas, Bobby Kleinberg, Quoc Le, Jon Kleinberg

Is Generator Conditioning Causally Related to GAN Performance?
Augustus OdenaJacob BuckmanCatherine OlssonTom BrownChristopher OlahColin RaffelIan Goodfellow

The Mirage of Action-Dependent Baselines in Reinforcement Learning
George TuckerSurya Bhupatiraju, Shixiang Gu, Richard E Turner, Zoubin Ghahramani, Sergey Levine

MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels
Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia LiLi Fei-Fei

Loss Decomposition for Fast Learning in Large Output Spaces
En-Hsu Yen, Satyen KaleFelix Xinnan YuDaniel Holtmann-RiceSanjiv Kumar, Pradeep Ravikumar

A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music
Adam RobertsJesse EngelColin RaffelCurtis HawthorneDouglas Eck

Smoothed Action Value Functions for Learning Gaussian Policies
Ofir NachumMohammad NorouziGeorge TuckerDale Schuurmans

Fast Decoding in Sequence Models Using Discrete Latent Variables
Lukasz KaiserSamy BengioAurko RoyAshish VaswaniNiki ParmarJakob UszkoreitNoam Shazeer

Accelerating Greedy Coordinate Descent Methods
Haihao Lu, Robert Freund, Vahab Mirrokni

Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions
Shuaiwen Wang, Wenda Zhou, Haihao Lu, Arian Maleki, Vahab Mirrokni

Image Transformer
Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Noam Shazeer, Alexander Ku, Dustin Tran

Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron
RJ Skerry-Ryan, Eric Battenberg, Ying Xiao, Yuxuan Wang, Daisy Stanton, Joel Shor, Ron Weiss, Robert Clark, Rif Saurous

Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks
Minmin Chen, Jeffrey Pennington,, Samuel Schoenholz

Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
Yuxuan Wang, Daisy Stanton, Yu Zhang, RJ Skerry-Ryan, Eric Battenberg, Joel ShorYing Xiao, Ye Jia, Fei Ren, Rif Saurous

Constrained Interacting Submodular Groupings
Andrew CotterMahdi Milani FardSeungil YouMaya Gupta, Jeff Bilmes

Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training
Xi Wu, Uyeong Jang, Jiefeng Chen, Lingjiao Chen, Somesh Jha

Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viégas, Rory Sayres

Online Learning with Abstention
Corinna CortesGiulia DeSalvoClaudio GentileMehryar Mohri, Scott Yang

Online Linear Quadratic Control
Alon CohenAvinatan HasidimTomer KorenNevena LazicYishay MansourKunal Talwar

Competitive Caching with Machine Learned Advice
Thodoris Lykouris, Sergei Vassilvitskii

Efficient Neural Audio Synthesis
Nal Kalchbrenner, Erich Elsen, Karen Simonyan, Seb Noury, Norman Casagrande, Edward Lockhart, Florian Stimberg, Aäron van den Oord, Sander Dieleman, Koray Kavukcuoglu

Gradient Descent with Identity Initialization Efficiently Learns Positive Definite Linear Transformations by Deep Residual Networks
Peter Bartlett, Dave Helmbold, Phil Long

Understanding and Simplifying One-Shot Architecture Search
Gabriel BenderPieter-Jan KindermansBarret ZophVijay VasudevanQuoc Le

Approximation Algorithms for Cascading Prediction Models
Matthew Streeter

Learning Longer-term Dependencies in RNNs with Auxiliary Losses
Trieu TrinhAndrew DaiThang LuongQuoc Le

Self-Imitation Learning
Junhyuk Oh, Yijie Guo, Satinder Singh, Honglak Lee

Adaptive Sampled Softmax with Kernel Based Sampling
Guy Blanc, Steffen Rendle

Workshops
2018 Workshop on Human Interpretability in Machine Learning (WHI)
Organizers: Been Kim, Kush Varshney, Adrian Weller
Invited Speakers include: Fernanda ViégasMartin Wattenberg

Exploration in Reinforcement Learning
Organizers: Ben EysenbachSurya BhupatirajuShane Gu, Junhyuk Oh, Vincent Vanhoucke, Oriol Vinyals, Doina Precup

Theoretical Foundations and Applications of Deep Generative Models
Invited speakers include: Honglak Lee

Source: Google AI Blog


Google at CVPR 2018

Posted by Christian Howard, Editor-in-Chief, Google AI Communications

This week, Salt Lake City hosts the 2018 Conference on Computer Vision and Pattern Recognition (CVPR 2018), 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 Diamond Sponsor, Google will have a strong presence at CVPR 2018 — over 200 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 portrait mode on the Pixel 2 and Pixel 2 XL smartphones, the Open Images V4 dataset and much more.

You can learn more about our research being presented at CVPR 2018 in the list below (Googlers highlighted in blue)

Organization
Finance Chair: Ramin Zabih

Area Chairs include: Sameer Agarwal, Aseem Agrawala, Jon Barron, Abhinav Shrivastava, Carl Vondrick, Ming-Hsuan Yang

Orals/Spotlights
Unsupervised Discovery of Object Landmarks as Structural Representations
Yuting Zhang, Yijie Guo, Yixin Jin, Yijun Luo, Zhiyuan He, Honglak Lee

DoubleFusion: Real-time Capture of Human Performances with Inner Body Shapes from a Single Depth Sensor
Tao Yu, Zerong Zheng, Kaiwen Guo, Jianhui Zhao, Qionghai Dai, Hao Li, Gerard Pons-Moll, Yebin Liu

Neural Kinematic Networks for Unsupervised Motion Retargetting
Ruben Villegas, Jimei Yang, Duygu Ceylan, Honglak Lee

Burst Denoising with Kernel Prediction Networks
Ben Mildenhall, Jiawen Chen, Jonathan BarronRobert Carroll, Dillon Sharlet, Ren Ng

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference Benoit Jacob, Skirmantas Kligys, Bo Chen, Matthew Tang, Menglong Zhu, Andrew Howard, Dmitry KalenichenkoHartwig Adam

AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
Chunhui Gu, Chen Sun, David Ross, Carl Vondrick, Caroline Pantofaru, Yeqing Li, Sudheendra Vijayanarasimhan, George Toderici, Susanna Ricco, Rahul Sukthankar, Cordelia Schmid, Jitendra Malik

Focal Visual-Text Attention for Visual Question Answering
Junwei Liang, Lu Jiang, Liangliang Cao, Li-Jia Li, Alexander G. Hauptmann

Inferring Light Fields from Shadows
Manel Baradad, Vickie Ye, Adam Yedida, Fredo Durand, William Freeman, Gregory Wornell, Antonio Torralba

Modifying Non-Local Variations Across Multiple Views
Tal Tlusty, Tomer Michaeli, Tali Dekel, Lihi Zelnik-Manor

Iterative Visual Reasoning Beyond Convolutions
Xinlei Chen, Li-jia Li, Fei-Fei Li, Abhinav Gupta

Unsupervised Training for 3D Morphable Model Regression
Kyle Genova, Forrester Cole, Aaron Maschinot, Daniel Vlasic, Aaron Sarna, William Freeman

Learning Transferable Architectures for Scalable Image Recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc Le

The iNaturalist Species Classification and Detection Dataset
Grant van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, Serge Belongie

Learning Intrinsic Image Decomposition from Watching the World
Zhengqi Li, Noah Snavely

Learning Intelligent Dialogs for Bounding Box Annotation
Ksenia Konyushkova, Jasper Uijlings, Christoph Lampert, Vittorio Ferrari

Posters
Revisiting Knowledge Transfer for Training Object Class Detectors
Jasper Uijlings, Stefan Popov, Vittorio Ferrari

Rethinking the Faster R-CNN Architecture for Temporal Action Localization
Yu-Wei Chao, Sudheendra Vijayanarasimhan, Bryan Seybold, David Ross, Jia Deng, Rahul Sukthankar

Hierarchical Novelty Detection for Visual Object Recognition
Kibok Lee, Kimin Lee, Kyle Min, Yuting Zhang, Jinwoo Shin, Honglak Lee

COCO-Stuff: Thing and Stuff Classes in Context
Holger Caesar, Jasper Uijlings, Vittorio Ferrari

Appearance-and-Relation Networks for Video Classification
Limin Wang, Wei Li, Wen Li, Luc Van Gool

MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks
Ariel Gordon, Elad Eban, Bo Chen, Ofir Nachum, Tien-Ju Yang, Edward Choi

Deformable Shape Completion with Graph Convolutional Autoencoders
Or Litany, Alex Bronstein, Michael Bronstein, Ameesh Makadia

MegaDepth: Learning Single-View Depth Prediction from Internet Photos
Zhengqi Li, Noah Snavely

Unsupervised Discovery of Object Landmarks as Structural Representations
Yuting Zhang, Yijie Guo, Yixin Jin, Yijun Luo, Zhiyuan He, Honglak Lee

Burst Denoising with Kernel Prediction Networks
Ben Mildenhall, Jiawen Chen, Jonathan Barron, Robert Carroll, Dillon Sharlet, Ren Ng

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference Benoit Jacob, Skirmantas Kligys, Bo Chen, Matthew Tang, Menglong Zhu, Andrew Howard, Dmitry Kalenichenko, Hartwig Adam

Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling
Xingyuan Sun, Jiajun Wu, Xiuming Zhang, Zhoutong Zhang, Tianfan Xue, Joshua Tenenbaum, William Freeman

Sparse, Smart Contours to Represent and Edit Images
Tali Dekel, Dilip Krishnan, Chuang Gan, Ce Liu, William Freeman

MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features
Liang-Chieh Chen, Alexander Hermans, George Papandreou, Florian Schroff, Peng Wang, Hartwig Adam

Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning
Yin Cui, Yang Song, Chen Sun, Andrew Howard, Serge Belongie

Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks
Nick Johnston, Damien Vincent, David Minnen, Michele Covell, Saurabh Singh, Sung Jin Hwang, George Toderici, Troy Chinen, Joel Shor

MobileNetV2: Inverted Residuals and Linear Bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen

ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans 
Angela Dai, Daniel Ritchie, Martin Bokeloh, Scott Reed, Juergen Sturm, Matthias Nießner

Sim2Real View Invariant Visual Servoing by Recurrent Control
Fereshteh Sadeghi, Alexander Toshev, Eric Jang, Sergey Levine

Alternating-Stereo VINS: Observability Analysis and Performance Evaluation
Mrinal Kanti Paul, Stergios Roumeliotis

Soccer on Your Tabletop
Konstantinos Rematas, Ira Kemelmacher, Brian Curless, Steve Seitz

Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints
Reza Mahjourian, Martin Wicke, Anelia Angelova

AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
Chunhui Gu, Chen Sun, David Ross, Carl Vondrick, Caroline Pantofaru, Yeqing Li, Sudheendra Vijayanarasimhan, George Toderici, Susanna Ricco, Rahul Sukthankar, Cordelia Schmid, Jitendra Malik

Inferring Light Fields from Shadows
Manel Baradad, Vickie Ye, Adam Yedida, Fredo Durand, William Freeman, Gregory Wornell, Antonio Torralba

Modifying Non-Local Variations Across Multiple Views
Tal Tlusty, Tomer Michaeli, Tali Dekel, Lihi Zelnik-Manor

Aperture Supervision for Monocular Depth Estimation
Pratul Srinivasan, Rahul Garg, Neal Wadhwa, Ren Ng, Jonathan Barron

Instance Embedding Transfer to Unsupervised Video Object Segmentation
Siyang Li, Bryan Seybold, Alexey Vorobyov, Alireza Fathi, Qin Huang, C.-C. Jay Kuo

Frame-Recurrent Video Super-Resolution
Mehdi S. M. Sajjadi, Raviteja Vemulapalli, Matthew Brown

Weakly Supervised Action Localization by Sparse Temporal Pooling Network
Phuc Nguyen, Ting Liu, Gautam Prasad, Bohyung Han

Iterative Visual Reasoning Beyond Convolutions
Xinlei Chen, Li-jia Li, Fei-Fei Li, Abhinav Gupta

Learning and Using the Arrow of Time
Donglai Wei, Andrew Zisserman, William Freeman, Joseph Lim

HydraNets: Specialized Dynamic Architectures for Efficient Inference
Ravi Teja Mullapudi, Noam Shazeer, William Mark, Kayvon Fatahalian

Thoracic Disease Identification and Localization with Limited Supervision
Zhe Li, Chong Wang, Mei Han, Yuan Xue, Wei Wei, Li-jia Li, Fei-Fei Li

Inferring Semantic Layout for Hierarchical Text-to-Image Synthesis
Seunghoon Hong, Dingdong Yang, Jongwook Choi, Honglak Lee

Deep Semantic Face Deblurring
Ziyi Shen, Wei-Sheng Lai, Tingfa Xu, Jan Kautz, Ming-Hsuan Yang

Unsupervised Training for 3D Morphable Model Regression
Kyle Genova, Forrester Cole, Aaron Maschinot, Daniel Vlasic, Aaron Sarna, William Freeman

Learning Transferable Architectures for Scalable Image Recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc Le

Learning Intrinsic Image Decomposition from Watching the World
Zhengqi Li, Noah Snavely

PiCANet: Learning Pixel-wise Contextual Attention for Saliency Detection
Nian Liu, Junwei Han, Ming-Hsuan Yang

Tutorials
Computer Vision for Robotics and Driving
Anelia Angelova, Sanja Fidler

Unsupervised Visual Learning
Pierre Sermanet, Anelia Angelova

UltraFast 3D Sensing, Reconstruction and Understanding of People, Objects and Environments
Sean Fanello, Julien Valentin, Jonathan Taylor, Christoph Rhemann, Adarsh Kowdle, Jürgen SturmChristine Kaeser-Chen, Pavel Pidlypenskyi, Rohit Pandey, Andrea Tagliasacchi, Sameh Khamis, David Kim, Mingsong Dou, Kaiwen Guo, Danhang Tang, Shahram Izadi

Generative Adversarial Networks
Jun-Yan Zhu, Taesung Park, Mihaela Rosca, Phillip Isola, Ian Goodfellow

Source: Google AI Blog


Announcing an updated YouTube-8M, and the 2nd YouTube-8M Large-Scale Video Understanding Challenge and Workshop



Last year, we organized the first YouTube-8M Large-Scale Video Understanding Challenge with Kaggle, in which 742 teams consisting of 946 individuals from 60 countries used the YouTube-8M dataset (2017 edition) to develop classification algorithms which accurately assign video-level labels. The purpose of the competition was to accelerate improvements in large-scale video understanding, representation learning, noisy data modeling, transfer learning and domain adaptation approaches that can help improve the machine-learning models that classify video. In addition to the competition, we hosted an affiliated workshop at CVPR’17, inviting competition top-performers and researchers and share their ideas on how to advance the state-of-the-art in video understanding.

As a continuation of these efforts to accelerate video understanding, we are excited to announce another update to the YouTube-8M dataset, a new Kaggle video understanding challenge and an affiliated 2nd Workshop on YouTube-8M Large-Scale Video Understanding, to be held at the 2018 European Conference on Computer Vision (ECCV'18).
An Updated YouTube-8M Dataset (2018 Edition)
Our YouTube-8M (2018 edition) features a major improvement in the quality of annotations, obtained using a machine learning system that combines audio-visual content with title, description and other metadata to provide more accurate ground truth annotations. The updated version contains 6.1 million URLs, labeled with a vocabulary of 3,862 visual entities, with each video annotated with one or more labels and an average of 3 labels per video. We have also updated the starter code, with updated instructions for downloading and training TensorFlow video annotation models on the dataset.

The 2nd YouTube-8M Video Understanding Challenge
The 2nd YouTube-8M Video Understanding Challenge invites participants to build audio-visual content classification models using YouTube-8M as training data, and then to label an unknown subset of test videos. Unlike last year, we strictly impose a hard limit on model size, encouraging participants to advance a single model within tight budget rather than assembling as many models as possible. Each of the top 5 teams will be awarded $5,000 to support their travel to Munich to attend ECCV’18. For details, please visit the Kaggle competition page.

The 2nd Workshop on YouTube-8M Large-Scale Video Understanding
To be held at ECCV’18, the workshop will consist of invited talks by distinguished researchers, as well as presentations by top-performing challenge participants in order to facilitate the exchange of ideas. We encourage those who wish to attend to submit papers describing their research, experiments, or applications based on YouTube-8M dataset, including papers summarizing their participation in the challenge above. Please refer to the workshop page for more details.

It is our hope that this update to the dataset, along with the new challenge and workshop, will continue to advance the research in large-scale video understanding. We hope you will join us again!

Acknowledgements
This post reflects the work of many machine perception researchers including Sami Abu-El-Haija, Ke Chen, Nisarg Kothari, Joonseok Lee, Hanhan Li, Paul Natsev, Sobhan Naderi Parizi, Rahul Sukthankar, George Toderici, Balakrishnan Varadarajan, as well as Sohier Dane, Julia Elliott, Wendy Kan and Walter Reade from Kaggle. We are also grateful for the support and advice from our partners at YouTube.

Source: Google AI Blog


Google at ICLR 2018



This week, Vancouver, Canada hosts the 6th International Conference on Learning Representations (ICLR 2018), 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 2018, Google will have a strong presence with over 130 researchers attending, 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 2018, 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 2018 in the list below (Googlers highlighted in blue)

Senior Program Chairs include:
Tara Sainath

Steering Committee includes:
Hugo Larochelle

Oral Contributions
Wasserstein Auto-Encoders
Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, Bernhard Scholkopf

On the Convergence of Adam and Beyond (Best Paper Award)
Sashank J. Reddi, Satyen Kale, Sanjiv Kumar

Ask the Right Questions: Active Question Reformulation with Reinforcement Learning
Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Wojciech Gajewski, Andrea Gesmundo, Neil Houlsby, Wei Wang

Beyond Word Importance: Contextual Decompositions to Extract Interactions from LSTMs
W. James Murdoch, Peter J. Liu, Bin Yu

Conference Posters
Boosting the Actor with Dual Critic
Bo Dai, Albert Shaw, Niao He, Lihong Li, Le Song

MaskGAN: Better Text Generation via Filling in the _______
William Fedus, Ian Goodfellow, Andrew M. Dai

Scalable Private Learning with PATE
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Ulfar Erlingsson

Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
Yujun Lin, Song Han, Huizi Mao, Yu Wang, William J. Dally

Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches
Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse

Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models
Adam Roberts, Jesse Engel, Matt Hoffman

Multi-Mention Learning for Reading Comprehension with Neural Cascades
Swabha Swayamdipta, Ankur P. Parikh, Tom Kwiatkowski

QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
Adams Wei Yu, David Dohan, Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, Quoc V. Le

Sensitivity and Generalization in Neural Networks: An Empirical Study
Roman Novak, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein

Action-dependent Control Variates for Policy Optimization via Stein Identity
Hao Liu, Yihao Feng, Yi Mao, Dengyong Zhou, Jian Peng, Qiang Liu

An Efficient Framework for Learning Sentence Representations
Lajanugen Logeswaran, Honglak Lee

Fidelity-Weighted Learning
Mostafa Dehghani, Arash Mehrjou, Stephan Gouws, Jaap Kamps, Bernhard Schölkopf

Generating Wikipedia by Summarizing Long Sequences
Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer

Matrix Capsules with EM Routing
Geoffrey Hinton, Sara Sabour, Nicholas Frosst

Temporal Difference Models: Model-Free Deep RL for Model-Based Control
Sergey Levine, Shixiang Gu, Murtaza Dalal, Vitchyr Pong

Deep Neural Networks as Gaussian Processes
Jaehoon Lee, Yasaman Bahri, Roman Novak, Samuel L. Schoenholz, Jeffrey Pennington, Jascha Sohl-Dickstein

Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence at Every Step
William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M. Dai, Shakir Mohamed, Ian Goodfellow

Initialization Matters: Orthogonal Predictive State Recurrent Neural Networks
Krzysztof Choromanski, Carlton Downey, Byron Boots

Learning Differentially Private Recurrent Language Models
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang

Learning Latent Permutations with Gumbel-Sinkhorn Networks
Gonzalo Mena, David Belanger, Scott Linderman, Jasper Snoek

Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning
Benjamin Eysenbach, Shixiang Gu, Julian IbarzSergey Levine

Meta-Learning for Semi-Supervised Few-Shot Classification
Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Josh Tenenbaum, Hugo Larochelle, Richard Zemel

Thermometer Encoding: One Hot Way to Resist Adversarial Examples
Jacob Buckman, Aurko Roy, Colin Raffel, Ian Goodfellow

A Hierarchical Model for Device Placement
Azalia Mirhoseini, Anna Goldie, Hieu Pham, Benoit Steiner, Quoc V. LeJeff Dean

Monotonic Chunkwise Attention
Chung-Cheng Chiu, Colin Raffel

Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples
Kimin Lee, Honglak Lee, Kibok Lee, Jinwoo Shin

Trust-PCL: An Off-Policy Trust Region Method for Continuous Control
Ofir Nachum, Mohammad Norouzi, Kelvin Xu, Dale Schuurmans

Ensemble Adversarial Training: Attacks and Defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, Patrick McDaniel

Stochastic Variational Video Prediction
Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy Campbell, Sergey Levine

Depthwise Separable Convolutions for Neural Machine Translation
Lukasz Kaiser, Aidan N. Gomez, Francois Chollet

Don’t Decay the Learning Rate, Increase the Batch Size
Samuel L. Smith, Pieter-Jan Kindermans, Chris Ying, Quoc V. Le

Generative Models of Visually Grounded Imagination
Ramakrishna Vedantam, Ian Fischer, Jonathan Huang, Kevin Murphy

Large Scale Distributed Neural Network Training through Online Distillation
Rohan Anil, Gabriel Pereyra, Alexandre Passos, Robert Ormandi, George E. Dahl, Geoffrey E. Hinton

Learning a Neural Response Metric for Retinal Prosthesis
Nishal P. Shah, Sasidhar Madugula, Alan Litke, Alexander Sher, EJ Chichilnisky, Yoram Singer, Jonathon Shlens

Neumann Optimizer: A Practical Optimization Algorithm for Deep Neural Networks
Shankar Krishnan, Ying Xiao, Rif A. Saurous

A Neural Representation of Sketch Drawings
David HaDouglas Eck

Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling
Carlos Riquelme, George Tucker, Jasper Snoek

Generalizing Hamiltonian Monte Carlo with Neural Networks
Daniel Levy, Matthew D. HoffmanJascha Sohl-Dickstein

Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis
Rudy Bunel, Matthew Hausknecht, Jacob Devlin, Rishabh Singh, Pushmeet Kohli

On the Discrimination-Generalization Tradeoff in GANs
Pengchuan Zhang, Qiang Liu, Dengyong Zhou, Tao Xu, Xiaodong He

A Bayesian Perspective on Generalization and Stochastic Gradient Descent
Samuel L. Smith, Quoc V. Le

Learning how to Explain Neural Networks: PatternNet and PatternAttribution
Pieter-Jan Kindermans, Kristof T. Schütt, Maximilian Alber, Klaus-Robert Müller, Dumitru Erhan, Been Kim, Sven Dähne

Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks
Víctor Campos, Brendan Jou, Xavier Giró-i-Nieto, Jordi Torres, Shih-Fu Chang

Towards Neural Phrase-based Machine Translation
Po-Sen Huang, Chong Wang, Sitao Huang, Dengyong Zhou, Li Deng

Unsupervised Cipher Cracking Using Discrete GANs
Aidan N. Gomez, Sicong Huang, Ivan Zhang, Bryan M. Li, Muhammad Osama, Lukasz Kaiser

Variational Image Compression With A Scale Hyperprior
Johannes Ballé, David Minnen, Saurabh Singh, Sung Jin Hwang, Nick Johnston

Workshop Posters
Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values
Julius Adebayo, Justin Gilmer, Ian Goodfellow, Been Kim

Stoachastic Gradient Langevin Dynamics that Exploit Neural Network Structure
Zachary Nado, Jasper Snoek, Bowen Xu, Roger Grosse, David Duvenaud, James Martens

Towards Mixed-initiative generation of multi-channel sequential structure
Anna Huang, Sherol Chen, Mark J. Nelson, Douglas Eck

Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?
Maithra Raghu, Alex Irpan, Jacob Andreas, Robert Kleinberg, Quoc V. Le, Jon Kleinberg

GILBO: One Metric to Measure Them All
Alexander Alemi, Ian Fischer

HoME: a Household Multimodal Environment
Simon Brodeur, Ethan Perez, Ankesh Anand, Florian Golemo, Luca Celotti, Florian Strub, Jean Rouat, Hugo Larochelle, Aaron Courville

Learning to Learn without Labels
Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein

Learning via Social Awareness: Improving Sketch Representations with Facial Feedback
Natasha Jaques, Jesse Engel, David Ha, Fred Bertsch, Rosalind Picard, Douglas Eck

Negative Eigenvalues of the Hessian in Deep Neural Networks
Guillaume Alain, Nicolas Le Roux, Pierre-Antoine Manzagol

Realistic Evaluation of Semi-Supervised Learning Algorithms
Avital Oliver, Augustus Odena, Colin Raffel, Ekin Cubuk, lan Goodfellow

Winner's Curse? On Pace, Progress, and Empirical Rigor
D. Sculley, Jasper Snoek, Alex Wiltschko, Ali Rahimi

Meta-Learning for Batch Mode Active Learning
Sachin Ravi, Hugo Larochelle

To Prune, or Not to Prune: Exploring the Efficacy of Pruning for Model Compression
Michael Zhu, Suyog Gupta

Adversarial Spheres
Justin Gilmer, Luke Metz, Fartash Faghri, Sam Schoenholz, Maithra Raghu,,Martin Wattenberg, Ian Goodfellow

Clustering Meets Implicit Generative Models
Francesco Locatello, Damien Vincent, Ilya Tolstikhin, Gunnar Ratsch, Sylvain Gelly, Bernhard Scholkopf

Decoding Decoders: Finding Optimal Representation Spaces for Unsupervised Similarity Tasks
Vitalii Zhelezniak, Dan Busbridge, April Shen, Samuel L. Smith, Nils Y. Hammerla

Learning Longer-term Dependencies in RNNs with Auxiliary Losses
Trieu Trinh, Quoc Le, Andrew Dai, Thang Luong

Graph Partition Neural Networks for Semi-Supervised Classification
Alexander Gaunt, Danny Tarlow, Marc Brockschmidt, Raquel Urtasun, Renjie Liao, Richard Zemel

Searching for Activation Functions
Prajit Ramachandran, Barret Zoph, Quoc Le

Time-Dependent Representation for Neural Event Sequence Prediction
Yang Li, Nan Du, Samy Bengio

Faster Discovery of Neural Architectures by Searching for Paths in a Large Model
Hieu Pham, Melody Guan, Barret Zoph, Quoc V. Le, Jeff Dean

Intriguing Properties of Adversarial Examples
Ekin Dogus Cubuk, Barret Zoph, Sam Schoenholz, Quoc Le

PPP-Net: Platform-aware Progressive Search for Pareto-optimal Neural Architectures
Jin-Dong Dong, An-Chieh Cheng, Da-Cheng Juan, Wei Wei, Min Sun

The Mirage of Action-Dependent Baselines in Reinforcement Learning
George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine

Learning to Organize Knowledge with N-Gram Machines
Fan Yang, Jiazhong Nie, William W. Cohen, Ni Lao

Online variance-reducing optimization
Nicolas Le Roux, Reza Babanezhad, Pierre-Antoine Manzagol

Google at ICLR 2018



This week, Vancouver, Canada hosts the 6th International Conference on Learning Representations (ICLR 2018), 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 2018, Google will have a strong presence with over 130 researchers attending, 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 2018, 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 2018 in the list below (Googlers highlighted in blue)

Senior Program Chair:
Tara Sainath

Steering Committee includes:
Hugo Larochelle

Oral Contributions
Wasserstein Auto-Encoders
Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, Bernhard Scholkopf

On the Convergence of Adam and Beyond (Best Paper Award)
Sashank J. Reddi, Satyen Kale, Sanjiv Kumar

Ask the Right Questions: Active Question Reformulation with Reinforcement Learning
Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Wojciech Gajewski, Andrea Gesmundo, Neil Houlsby, Wei Wang

Beyond Word Importance: Contextual Decompositions to Extract Interactions from LSTMs
W. James Murdoch, Peter J. Liu, Bin Yu

Conference Posters
Boosting the Actor with Dual Critic
Bo Dai, Albert Shaw, Niao He, Lihong Li, Le Song

MaskGAN: Better Text Generation via Filling in the _______
William Fedus, Ian Goodfellow, Andrew M. Dai

Scalable Private Learning with PATE
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Ulfar Erlingsson

Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
Yujun Lin, Song Han, Huizi Mao, Yu Wang, William J. Dally

Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches
Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse

Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models
Adam Roberts, Jesse Engel, Matt Hoffman

Multi-Mention Learning for Reading Comprehension with Neural Cascades
Swabha Swayamdipta, Ankur P. Parikh, Tom Kwiatkowski

QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
Adams Wei Yu, David Dohan, Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, Quoc V. Le

Sensitivity and Generalization in Neural Networks: An Empirical Study
Roman Novak, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein

Action-dependent Control Variates for Policy Optimization via Stein Identity
Hao Liu, Yihao Feng, Yi Mao, Dengyong Zhou, Jian Peng, Qiang Liu

An Efficient Framework for Learning Sentence Representations
Lajanugen Logeswaran, Honglak Lee

Fidelity-Weighted Learning
Mostafa Dehghani, Arash Mehrjou, Stephan Gouws, Jaap Kamps, Bernhard Schölkopf

Generating Wikipedia by Summarizing Long Sequences
Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer

Matrix Capsules with EM Routing
Geoffrey Hinton, Sara Sabour, Nicholas Frosst

Temporal Difference Models: Model-Free Deep RL for Model-Based Control
Sergey Levine, Shixiang Gu, Murtaza Dalal, Vitchyr Pong

Deep Neural Networks as Gaussian Processes
Jaehoon Lee, Yasaman Bahri, Roman Novak, Samuel L. Schoenholz, Jeffrey Pennington, Jascha Sohl-Dickstein

Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence at Every Step
William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M. Dai, Shakir Mohamed, Ian Goodfellow

Initialization Matters: Orthogonal Predictive State Recurrent Neural Networks
Krzysztof Choromanski, Carlton Downey, Byron Boots

Learning Differentially Private Recurrent Language Models
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang

Learning Latent Permutations with Gumbel-Sinkhorn Networks
Gonzalo Mena, David Belanger, Scott Linderman, Jasper Snoek

Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning
Benjamin Eysenbach, Shixiang Gu, Julian IbarzSergey Levine

Meta-Learning for Semi-Supervised Few-Shot Classification
Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Josh Tenenbaum, Hugo Larochelle, Richard Zemel

Thermometer Encoding: One Hot Way to Resist Adversarial Examples
Jacob Buckman, Aurko Roy, Colin Raffel, Ian Goodfellow

A Hierarchical Model for Device Placement
Azalia Mirhoseini, Anna Goldie, Hieu Pham, Benoit Steiner, Quoc V. LeJeff Dean

Monotonic Chunkwise Attention
Chung-Cheng Chiu, Colin Raffel

Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples
Kimin Lee, Honglak Lee, Kibok Lee, Jinwoo Shin

Trust-PCL: An Off-Policy Trust Region Method for Continuous Control
Ofir Nachum, Mohammad Norouzi, Kelvin Xu, Dale Schuurmans

Ensemble Adversarial Training: Attacks and Defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, Patrick McDaniel

Stochastic Variational Video Prediction
Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy Campbell, Sergey Levine

Depthwise Separable Convolutions for Neural Machine Translation
Lukasz Kaiser, Aidan N. Gomez, Francois Chollet

Don’t Decay the Learning Rate, Increase the Batch Size
Samuel L. Smith, Pieter-Jan Kindermans, Chris Ying, Quoc V. Le

Generative Models of Visually Grounded Imagination
Ramakrishna Vedantam, Ian Fischer, Jonathan Huang, Kevin Murphy

Large Scale Distributed Neural Network Training through Online Distillation
Rohan Anil, Gabriel Pereyra, Alexandre Passos, Robert Ormandi, George E. Dahl, Geoffrey E. Hinton

Learning a Neural Response Metric for Retinal Prosthesis
Nishal P. Shah, Sasidhar Madugula, Alan Litke, Alexander Sher, EJ Chichilnisky, Yoram Singer, Jonathon Shlens

Neumann Optimizer: A Practical Optimization Algorithm for Deep Neural Networks
Shankar Krishnan, Ying Xiao, Rif A. Saurous

A Neural Representation of Sketch Drawings
David HaDouglas Eck

Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling
Carlos Riquelme, George Tucker, Jasper Snoek

Generalizing Hamiltonian Monte Carlo with Neural Networks
Daniel Levy, Matthew D. HoffmanJascha Sohl-Dickstein

Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis
Rudy Bunel, Matthew Hausknecht, Jacob Devlin, Rishabh Singh, Pushmeet Kohli

On the Discrimination-Generalization Tradeoff in GANs
Pengchuan Zhang, Qiang Liu, Dengyong Zhou, Tao Xu, Xiaodong He

A Bayesian Perspective on Generalization and Stochastic Gradient Descent
Samuel L. Smith, Quoc V. Le

Learning how to Explain Neural Networks: PatternNet and PatternAttribution
Pieter-Jan Kindermans, Kristof T. Schütt, Maximilian Alber, Klaus-Robert Müller, Dumitru Erhan, Been Kim, Sven Dähne

Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks
Víctor Campos, Brendan Jou, Xavier Giró-i-Nieto, Jordi Torres, Shih-Fu Chang

Towards Neural Phrase-based Machine Translation
Po-Sen Huang, Chong Wang, Sitao Huang, Dengyong Zhou, Li Deng

Unsupervised Cipher Cracking Using Discrete GANs
Aidan N. Gomez, Sicong Huang, Ivan Zhang, Bryan M. Li, Muhammad Osama, Lukasz Kaiser

Variational Image Compression With A Scale Hyperprior
Johannes Ballé, David Minnen, Saurabh Singh, Sung Jin Hwang, Nick Johnston

Workshop Posters
Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values
Julius Adebayo, Justin Gilmer, Ian Goodfellow, Been Kim

Stoachastic Gradient Langevin Dynamics that Exploit Neural Network Structure
Zachary Nado, Jasper Snoek, Bowen Xu, Roger Grosse, David Duvenaud, James Martens

Towards Mixed-initiative generation of multi-channel sequential structure
Anna Huang, Sherol Chen, Mark J. Nelson, Douglas Eck

Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?
Maithra Raghu, Alex Irpan, Jacob Andreas, Robert Kleinberg, Quoc V. Le, Jon Kleinberg

GILBO: One Metric to Measure Them All
Alexander Alemi, Ian Fischer

HoME: a Household Multimodal Environment
Simon Brodeur, Ethan Perez, Ankesh Anand, Florian Golemo, Luca Celotti, Florian Strub, Jean Rouat, Hugo Larochelle, Aaron Courville

Learning to Learn without Labels
Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein

Learning via Social Awareness: Improving Sketch Representations with Facial Feedback
Natasha Jaques, Jesse Engel, David Ha, Fred Bertsch, Rosalind Picard, Douglas Eck

Negative Eigenvalues of the Hessian in Deep Neural Networks
Guillaume Alain, Nicolas Le Roux, Pierre-Antoine Manzagol

Realistic Evaluation of Semi-Supervised Learning Algorithms
Avital Oliver, Augustus Odena, Colin Raffel, Ekin Cubuk, lan Goodfellow

Winner's Curse? On Pace, Progress, and Empirical Rigor
D. Sculley, Jasper Snoek, Alex Wiltschko, Ali Rahimi

Meta-Learning for Batch Mode Active Learning
Sachin Ravi, Hugo Larochelle

To Prune, or Not to Prune: Exploring the Efficacy of Pruning for Model Compression
Michael Zhu, Suyog Gupta

Adversarial Spheres
Justin Gilmer, Luke Metz, Fartash Faghri, Sam Schoenholz, Maithra Raghu,,Martin Wattenberg, Ian Goodfellow

Clustering Meets Implicit Generative Models
Francesco Locatello, Damien Vincent, Ilya Tolstikhin, Gunnar Ratsch, Sylvain Gelly, Bernhard Scholkopf

Decoding Decoders: Finding Optimal Representation Spaces for Unsupervised Similarity Tasks
Vitalii Zhelezniak, Dan Busbridge, April Shen, Samuel L. Smith, Nils Y. Hammerla

Learning Longer-term Dependencies in RNNs with Auxiliary Losses
Trieu Trinh, Quoc Le, Andrew Dai, Thang Luong

Graph Partition Neural Networks for Semi-Supervised Classification
Alexander Gaunt, Danny Tarlow, Marc Brockschmidt, Raquel Urtasun, Renjie Liao, Richard Zemel

Searching for Activation Functions
Prajit Ramachandran, Barret Zoph, Quoc Le

Time-Dependent Representation for Neural Event Sequence Prediction
Yang Li, Nan Du, Samy Bengio

Faster Discovery of Neural Architectures by Searching for Paths in a Large Model
Hieu Pham, Melody Guan, Barret Zoph, Quoc V. Le, Jeff Dean

Intriguing Properties of Adversarial Examples
Ekin Dogus Cubuk, Barret Zoph, Sam Schoenholz, Quoc Le

PPP-Net: Platform-aware Progressive Search for Pareto-optimal Neural Architectures
Jin-Dong Dong, An-Chieh Cheng, Da-Cheng Juan, Wei Wei, Min Sun

The Mirage of Action-Dependent Baselines in Reinforcement Learning
George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine

Learning to Organize Knowledge with N-Gram Machines
Fan Yang, Jiazhong Nie, William W. Cohen, Ni Lao

Online variance-reducing optimization
Nicolas Le Roux, Reza Babanezhad, Pierre-Antoine Manzagol

Source: Google AI Blog


Googlers on the road: FOSSASIA Summit 2018

In a week’s time, free and open source enthusiasts of all kinds will gather in Singapore for FOSSASIA Summit 2018. Established in 2009, the annual event attracts more than 3,000 attendees, running from March 22nd to 25th this year.

FOSSASIA logo licensed LGPL-2.1.
FOSSASIA Summit is organized by FOSSASIA, a nonprofit organization that focuses on Asia and brings people together around open technology both in-person and online. The organization is also home to many open source projects and is a regular participant in the Google Open Source team’s student programs, Google Summer of Code and Google Code-in.

Our team is excited to be among those attending and speaking at the conference this year, and we’re proud that Google Cloud is a sponsor. If you’re around, please come say hello. The highlight of our travel is meeting the students and mentors who have participated in our programs!

Here are the Googlers who will be giving presentations:

Thursday, March 22nd

2:00pm Real-world Machine Learning with TensorFlow and Cloud ML by Kaz Sato

Friday, March 23rd

9:30am BigQuery codelab by Jan Peuker
10:30am  Working with Cloud DataPrep by KC Ayyagari
1:00pm Extract, analyze & translate Text from Images with Cloud ML APIs by Sara Robinson
2:00pm    Bitcoin in BigQuery: blockchain analytics on public data by Allen Day
2:40pm What can we learn from 1.1 billion GitHub events and 42 TB of code? by Felipe Hoffa2:40pm Engaging IoT solutions with Machine Learning by Markku Lepisto
2:45pm CloudML Engine: Qwik Start by Kaz Sato
3:20pm Systems as choreographed behavior with Kubernetes by Jan Peuker
4:00pm The Assistant by Manikantan Krishnamurthy

Saturday, March 24th

10:30am Google Summer of Code and Google Code-in by Stephanie Taylor
10:30am Zero to ML on Google Cloud Platform by Sara Robinson
11:00am  Building a Sustainable Open Tech Community through Coding Programs, Contests and Hackathons panel including Stephanie Taylor
11:05am Codifying Security and Modern Secrets Management by Seth Vargo
1:00pm    Open Source Education panel including Cat Allman
5:00pm Everything as Code by Seth Vargo

We look forward to seeing you there!

By Josh Simmons, Google Open Source

Googlers on the road: FOSDEM 2018

The Google Open Source team is currently enjoying summer weather in Sydney at Linux.conf.au, but soon we return to winter weather and head to Brussels for FOSDEM 2018. FOSDEM is a special event, famous for being non-commercial, volunteer-organized, and free to attend. It’s also huge, attracting more than 5,000 attendees.

FOSDEM logo licensed CC BY 2.0 SE.
This year FOSDEM is particularly special as it falls on top of the 20th anniversary of the open source movement and its steward, the Open Source Initiative. (In case you’re wondering, this September will mark the 35th anniversary of the free software movement.) We’re looking forward to celebrating the occasion!

You’ll find us in the hallways, at satellite events, and at our table in the stands area. You’ll also find some Googlers in the conference schedule, as well as folks sharing their experience of the most recent Google Summer of Code and Google Code-in.

If you’d like to say hello or chat, swing by our table in Building K. The highlight of our trip is meeting hundreds of the thousands of students and mentors who have participated in our programs!

Below are the Googlers who will be giving presentations:

Saturday, February 3rd
12:30pm  Google’s approach to distributed systems observability for Go by JBD (also at 2:30pm)
3:05pm   Testing and Validating distributed systems by Holden Karau

Sunday, February 4th
10:20am  Regular Expression Derivatives in Python by Michael Paddon
11:30am   Advocating For FOSS Inside Companies a panel including Max Sills
3:00pm    Your Build in a Datacenter by Jakob Buchgraber
4:00pm    Accelerating Big Data Outside of the JVM by Holden Karau

Hope to see you there!

By Josh Simmons, Google Open Source