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Google at ICML 2020

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-seventh International Conference on Machine Learning (ICML 2020), a premier annual event taking place virtually this week. With over 100 accepted publications and Googlers participating in workshops, we look forward to our continued collaboration with the larger machine learning research community.

If you're registered for ICML 2020, we hope you'll visit the Google virtual booth to learn more about the exciting work, creativity and fun that goes into solving some of the field's most interesting challenges. You can also learn more about the Google research being presented at ICML 2020 in the list below (Google affiliations bolded).

Google Dataset Search: Building an Open Ecosystem for Dataset Discovery
Natasha Noy

End-to-end Bayesian inference workflows in TensorFlow Probability
Colin Carroll

Population-Based Black-Box Optimization for Biological Sequence Design
Christof Angermueller, David Belanger, Andreea Gane, Zelda Mariet, David Dohan, Kevin Murphy, Lucy Colwell, D Sculley

Predictive Coding for Locally-Linear Control
Rui Shu, Tung Nguyen, Yinlam Chow, Tuan Pham, Khoat Than, Mohammad Ghavamzadeh, Stefano Ermon, Hung Bui

FedBoost: A Communication-Efficient Algorithm for Federated Learning
Jenny Hamer, Mehryar Mohri, Ananda Theertha Suresh

Faster Graph Embeddings via Coarsening
Matthew Fahrbach, Gramoz Goranci, Richard Peng, Sushant Sachdeva, Chi Wang

Revisiting Fundamentals of Experience Replay
William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney

Boosting for Control of Dynamical Systems
Naman Agarwal, Nataly Brukhim, Elad Hazan, Zhou Lu

Neural Clustering Processes
Ari Pakman, Yueqi Wang, Catalin Mitelut, JinHyung Lee, Liam Paninski

The Tree Ensemble Layer: Differentiability Meets Conditional Computation
Hussein Hazimeh, Natalia Ponomareva, Petros Mol, Zhenyu Tan, Rahul Mazumder

Representations for Stable Off-Policy Reinforcement Learning
Dibya Ghosh, Marc Bellemare

REALM: Retrieval-Augmented Language Model Pre-Training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, Ming-Wei Chang

Context Aware Local Differential Privacy
Jayadev Acharya, Keith Bonawitz, Peter Kairouz, Daniel Ramage, Ziteng Sun

Scalable Deep Generative Modeling for Sparse Graphs
Hanjun Dai, Azade Nazi, Yujia Li, Bo Dai, Dale Schuurmans

Deep k-NN for Noisy Labels
Dara Bahri, Heinrich Jiang, Maya Gupta

Revisiting Spatial Invariance with Low-Rank Local Connectivity
Gamaleldin F. Elsayed, Prajit Ramachandran, Jonathon Shlens, Simon Kornblith

SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh

Incremental Sampling Without Replacement for Sequence Models
Kensen Shi, David Bieber, Charles Sutton

SoftSort: A Continuous Relaxation for the argsort Operator
Sebastian Prillo, Julian Martin Eisenschlos

XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalisation (see blog post)
Junjie Hu, Sebastian Ruder, Aditya Siddhant, Graham Neubig, Orhan Firat, Melvin Johnson

Learning to Stop While Learning to Predict
Xinshi Chen, Hanjun Dai, Yu Li, Xin Gao, Le Song

Bandits with Adversarial Scaling
Thodoris Lykouris, Vahab Mirrokni, Renato Paes Leme

SimGANs: Simulator-Based Generative Adversarial Networks for ECG Synthesis to Improve Deep ECG Classification
Tomer Golany, Daniel Freedman, Kira Radinsky

Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization
Geoffrey Negiar, Gideon Dresdner, Alicia Yi-Ting Tsai, Laurent El Ghaoui, Francesco Locatello, Robert M. Freund, Fabian Pedregosa

Implicit differentiation of Lasso-type models for hyperparameter optimization
Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon

Infinite attention: NNGP and NTK for deep attention networks
Jiri Hron, Yasaman Bahri, Jascha Sohl-Dickstein, Roman Novak

Logarithmic Regret for Learning Linear Quadratic Regulators Efficiently
Asaf Cassel, Alon Cohen, Tomer Koren

Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks
Pranjal Awasthi, Natalie Frank, Mehryar Mohri

Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization
Daniel Golovin, Qiuyi (Richard) Zhang

Generating Programmatic Referring Expressions via Program Synthesis
Jiani Huang, Calvin Smith, Osbert Bastani, Rishabh Singh, Aws Albarghouthi, Mayur Naik

Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach
Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard Zemel, Craig Boutilier

AutoML-Zero: Evolving Machine Learning Algorithms From Scratch (see blog post)
Esteban Real, Chen Liang, David R. So, Quoc V. Le

How Good is the Bayes Posterior in Deep Neural Networks Really?
Florian Wenzel, Kevin Roth, Bastiaan S. Veeling, Jakub Swiatkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin

Which Tasks Should Be Learned Together in Multi-task Learning?
Trevor Standley, Amir R. Zamir, Dawn Chen, Leonidas Guibas, Jitendra Malik, Silvio Savarese

Influence Diagram Bandits: Variational Thompson Sampling for Structured Bandit Problems
Tong Yu, Branislav Kveton, Zheng Wen, Ruiyi Zhang, Ole J. Mengshoel

Disentangling Trainability and Generalization in Deep Neural Networks
Lechao Xiao, Jeffrey Pennington, Samuel S. Schoenholz

The Many Shapley Values for Model Explanation
Mukund Sundararajan, Amir Najmi

Neural Contextual Bandits with UCB-based Exploration
Dongruo Zhou, Lihong Li, Quanquan Gu

Automatic Shortcut Removal for Self-Supervised Representation Learning
Matthias Minderer, Olivier Bachem, Neil Houlsby, Michael Tschannen

Federated Learning with Only Positive Labels
Felix X. Yu, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar

How Recurrent Networks Implement Contextual Processing in Sentiment Analysis
Niru Maheswaranathan, David Sussillo

Supervised Learning: No Loss No Cry
Richard Nock, Aditya Krishna Menon

Ready Policy One: World Building Through Active Learning
Philip Ball, Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen Roberts

Weakly-Supervised Disentanglement Without Compromises
Francesco Locatello, Ben Poole, Gunnar Raetsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen

Fast Differentiable Sorting and Ranking
Mathieu Blondel, Olivier Teboul, Quentin Berthet, Josip Djolonga

Debiased Sinkhorn barycenters
Hicham Janati, Marco Cuturi, Alexandre Gramfort

Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure
John Sipple

Accelerating Large-Scale Inference with Anisotropic Vector Quantization
Ruiqi Guo, Philip Sun, Erik Lindgren, Quan Geng, David Simcha, Felix Chern, Sanjiv Kumar

An Optimistic Perspective on Offline Reinforcement Learning (see blog post)
Rishabh Agarwal, Dale Schuurmans, Mohammad Norouzi

The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization
Ben Adlam, Jeffrey Pennington

Private Query Release Assisted by Public Data
Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan Ullman, Zhiwei Steven Wu

Learning and Evaluating Contextual Embedding of Source Code
Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, Kensen Shi

Evaluating Machine Accuracy on ImageNet
Vaishaal Shankar, Rebecca Roelofs, Horia Mania, Alex Fang, Benjamin Recht, Ludwig Schmidt

Imputer: Sequence Modelling via Imputation and Dynamic Programming
William Chan, Chitwan Saharia, Geoffrey Hinton, Mohammad Norouzi, Navdeep Jaitly

Domain Aggregation Networks for Multi-Source Domain Adaptation
Junfeng Wen, Russell Greiner, Dale Schuurmans

Planning to Explore via Self-Supervised World Models
Ramanan Sekar, Oleh Rybkin, Kostas Daniilidis, Pieter Abbeel, Danijar Hafner, Deepak Pathak

Context-Aware Dynamics Model for Generalization in Model-Based Reinforcement Learning
Kimin Lee, Younggyo Seo, Seunghyun Lee, Honglak Lee, Jinwoo Shin

Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search
Binghong Chen, Chengtao Li, Hanjun Dai, Le Song

On the Consistency of Top-k Surrogate Losses
Forest Yang, Sanmi Koyejo

Dual Mirror Descent for Online Allocation Problems
Haihao Lu, Santiago Balseiro, Vahab Mirrokni

Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors
Michael W. Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-An Ma, Jasper Snoek, Katherine Heller, Balaji Lakshminarayanan, Dustin Tran

Batch Stationary Distribution Estimation
Junfeng Wen, Bo Dai, Lihong Li, Dale Schuurmans

Small-GAN: Speeding Up GAN Training Using Core-Sets
Samarth Sinha, Han Zhang, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, Augustus Odena

Data Valuation Using Reinforcement Learning
Jinsung Yoon, Sercan ‎Ö. Arik, Tomas Pfister

A Game Theoretic Perspective on Model-Based Reinforcement Learning
Aravind Rajeswaran, Igor Mordatch, Vikash Kumar

Encoding Musical Style with Transformer Autoencoders
Kristy Choi, Curtis Hawthorne, Ian Simon, Monica Dinculescu, Jesse Engel

The Shapley Taylor Interaction Index
Kedar Dhamdhere, Mukund Sundararajan, Ashish Agarwal

Multidimensional Shape Constraints
Maya Gupta, Erez Louidor, Olexander Mangylov, Nobu Morioka, Taman Narayan, Sen Zhao

Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead
Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh

Learning to Score Behaviors for Guided Policy Optimization
Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Anna Choromanska, Krzysztof Choromanski, Michael I. Jordan

Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations
Florian Tramèr, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, Jörn-Henrik Jacobsen

Optimizing Black-Box Metrics with Adaptive Surrogates
Qijia Jiang, Olaoluwa Adigun, Harikrishna Narasimhan, Mahdi Milani Fard, Maya Gupta

Circuit-Based Intrinsic Methods to Detect Overfitting
Sat Chatterjee, Alan Mishchenko

Automatic Reparameterisation of Probabilistic Programs
Maria I. Gorinova, Dave Moore, Matthew D. Hoffman

Stochastic Flows and Geometric Optimization on the Orthogonal Group
Krzysztof Choromanski, David Cheikhi, Jared Davis, Valerii Likhosherstov, Achille Nazaret, Achraf Bahamou, Xingyou Song, Mrugank Akarte, Jack Parker-Holder, Jacob Bergquist, Yuan Gao, Aldo Pacchiano, Tamas Sarlos, Adrian Weller, Vikas Sindhwani

Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics
Matthew Hoffman, Yi-An Ma

Concise Explanations of Neural Networks Using Adversarial Training
Prasad Chalasani, Jiefeng Chen, Amrita Roy Chowdhury, Somesh Jha, Xi Wu

p-Norm Flow Diffusion for Local Graph Clustering
Shenghao Yang, Di Wang, Kimon Fountoulakis

Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models
Rares-Darius Buhai, Yoni Halpern, Yoon Kim, Andrej Risteski, David Sontag

Robust Pricing in Dynamic Mechanism Design
Yuan Deng, Sébastien Lahaie, Vahab Mirrokni

Differentiable Product Quantization for Learning Compact Embedding Layers
Ting Chen, Lala Li, Yizhou Sun

Adaptive Region-Based Active Learning
Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Ningshan Zhang

Countering Language Drift with Seeded Iterated Learning
Yuchen Lu, Soumye Singhal, Florian Strub, Olivier Pietquin, Aaron Courville

Does Label Smoothing Mitigate Label Noise?
Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon, Sanjiv Kumar

Acceleration Through Spectral Density Estimation
Fabian Pedregosa, Damien Scieur

Momentum Improves Normalized SGD
Ashok Cutkosky, Harsh Mehta

ConQUR: Mitigating Delusional Bias in Deep Q-Learning
Andy Su, Jayden Ooi, Tyler Lu, Dale Schuurmans, Craig Boutilier

Online Learning with Imperfect Hints
Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit

Go Wide, Then Narrow: Efficient Training of Deep Thin Networks
Denny Zhou, Mao Ye, Chen Chen, Tianjian Meng, Mingxing Tan, Xiaodan Song, Quoc Le, Qiang Liu, Dale Schuurmans

On Implicit Regularization in β-VAEs
Abhishek Kumar, Ben Poole

Is Local SGD Better than Minibatch SGD?
Blake Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, Nathan Sreb

A Simple Framework for Contrastive Learning of Visual Representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, Geoffrey Hinton

Universal Average-Case Optimality of Polyak Momentum
Damien Scieur, Fabian Pedregosa

An Imitation Learning Approach for Cache Replacement
Evan Zheran Liu, Milad Hashemi, Kevin Swersky, Parthasarathy Ranganathan, Junwhan Ahn

Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems
Zhe Dong, Bryan A. Seybold, Kevin P. Murphy, Hung H. Bui

Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels
Lu Jiang, Di Huang, Mason Liu, Weilong Yang

Optimizing Data Usage via Differentiable Rewards
Xinyi Wang, Hieu Pham, Paul Michel, Antonios Anastasopoulos, Jaime Carbonell, Graham Neubig

Sparse Sinkhorn Attention
Yi Tay, Dara Bahri, Liu Yang, Donald Metzler, Da-Cheng Juan

One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control
Wenlong Huang, Igor Mordatch, Deepak Pathak

On Thompson Sampling with Langevin Algorithms
Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan

Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection
Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam Klivans, Qiang Liu

On the Global Convergence Rates of Softmax Policy Gradient Methods
Jincheng Mei, Chenjun Xiao, Csaba Szepesvari, Dale Schuurmans

Concept Bottleneck Models
Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang

Supervised Quantile Normalization for Low-Rank Matrix Approximation
Marco Cuturi, Olivier Teboul, Jonathan Niles-Weed, Jean-Philippe Vert

Missing Data Imputation Using Optimal Transport
Boris Muzellec, Julie Josse, Claire Boyer, Marco Cuturi

Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention Over Modules
Sarthak Mittal, Alex Lamb, Anirudh Goyal, Vikram Voleti, Murray Shanahan, Guillaume Lajoie, Michael Mozer, Yoshua Bengio

Stochastic Optimization for Regularized Wasserstein Estimators
Marin Ballu, Quentin Berthet, Francis Bach

Low-Rank Bottleneck in Multi-head Attention Models
Srinadh Bhojanapalli, Chulhee Yun, Ankit Singh Rawat, Sashank Jakkam Reddi, Sanjiv Kumar

Rigging the Lottery: Making All Tickets Winners
Utku Evci, Trevor Gale, Jacob Menick, Pablo Samuel Castro, Erich Elsen

Online Learning with Dependent Stochastic Feedback Graphs
Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Ningshan Zhang

Calibration, Entropy Rates, and Memory in Language Models
Mark Braverman, Xinyi Chen, Sham Kakade, Karthik Narasimhan, Cyril Zhang, Yi Zhang

Composable Sketches for Functions of Frequencies: Beyond the Worst Case
Edith Cohen, Ofir Geri, Rasmus Pagh

Energy-Based Processes for Exchangeable Data
Mengjiao Yang, Bo Dai, Hanjun Dai, Dale Schuurmans

Near-Optimal Regret Bounds for Stochastic Shortest Path
Alon Cohen, Haim Kaplan, Yishay Mansour, Aviv Rosenberg

PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization (see blog post)
Jingqing Zhang, Yao Zhao, Mohammad Saleh, Peter J. Liu

The Complexity of Finding Stationary Points with Stochastic Gradient Descent
Yoel Drori, Ohad Shamir

The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
Jakub Swiatkowski, Kevin Roth, Bas Veeling, Linh Tran, Josh Dillon, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin

Regularized Optimal Transport is Ground Cost Adversarial
François-Pierre Paty, Marco Cuturi

New In ML
Invited Speaker: Nicolas Le Roux
Organizers: Zhen Xu, Sparkle Russell-Puleri, Zhengying Liu, Sinead A Williamson, Matthias W Seeger, Wei-Wei Tu, Samy Bengio, Isabelle Guyon

LatinX in AI
Workshop Advisor: Pablo Samuel Castro

Women in Machine Learning Un-Workshop
Invited Speaker: Doina Precup
Sponsor Expo Speaker: Jennifer Wei

Queer in AI
Invited Speaker: Shakir Mohamed

Workshop on Continual Learning
Organizers: Haytham Fayek, Arslan Chaudhry, David Lopez-Paz, Eugene Belilovsky, Jonathan Schwarz, Marc Pickett, Rahaf Aljundi, Sayna Ebrahimi, Razvan Pascanu, Puneet Dokania

5th ICML Workshop on Human Interpretability in Machine Learning (WHI)
Organizers: Kush Varshney, Adrian Weller, Alice Xiang, Amit Dhurandhar, Been Kim, Dennis Wei, Umang Bhatt

Self-supervision in Audio and Speech
Organizers: Mirco Ravanelli, Dmitriy Serdyuk, R Devon Hjelm, Bhuvana Ramabhadran, Titouan Parcollet

Workshop on eXtreme Classification: Theory and Applications
Invited Speakers: Sanjiv Kumar

Healthcare Systems, Population Health, and the Role of Health-tech
Organizers: Krzysztof Choromanski, David Cheikhi, Jared Davis, Valerii Likhosherstov, Achille Nazaret, Achraf Bahamou, Xingyou Song, Mrugank Akarte, Jack Parker-Holder, Jacob Bergquist, Yuan Gao, Aldo Pacchiano, Tamas Sarlos, Adrian Weller, Vikas Sindhwani

Theoretical Foundations of Reinforcement Learning
Program Committee: Alon Cohen, Chris Dann

Uncertainty and Robustness in Deep Learning Workshop (UDL)
Invited Speaker: Justin Gilmer

Organizers: Sharon Li, Balaji Lakshminarayanan, Dan Hendrycks, Thomas Dietterich, Jasper Snoek
Program Committee: Jeremiah Liu, Jie Ren, Rodolphe Jenatton, Zack Nado, Alexander Alemi, Florian Wenzel, Mike Dusenberry, Raphael Lopes

Beyond First Order Methods in Machine Learning Systems
Industry Panel: Jonathan Hseu

Object-Oriented Learning: Perception, Representation, and Reasoning
Invited Speakers: Thomas Kipf, Igor Mordatch

Graph Representation Learning and Beyond (GRL+)
Organizers: Michael Bronstein, Andreea Deac, William L. Hamilton, Jessica B. Hamrick, Milad Hashemi, Stefanie Jegelka, Jure Leskovec, Renjie Liao, Federico Monti, Yizhou Sun, Kevin Swersky, Petar Veličković, Rex Ying, Marinka Žitnik
Speakers: Thomas Kipf
Program Committee: Bryan Perozzi, Kevin Swersky, Milad Hashemi, Thomas Kipf, Ting Cheng

ML Interpretability for Scientific Discovery
Organizers: Subhashini Venugopalan, Michael Brenner, Scott Linderman, Been Kim
Program Committee: Akinori Mitani, Arunachalam Narayanaswamy, Avinash Varadarajan, Awa Dieng, Benjamin Sanchez-Lengeling, Bo Dai, Stephan Hoyer, Subham Sekhar Sahoo, Suhani Vora
Steering Committee: John Platt, Mukund Sundararajan, Jon Kleinberg

Negative Dependence and Submodularity for Machine Learning
Organizers: Zelda Mariet, Mike Gartrell, Michal Derezinski

7th ICML Workshop on Automated Machine Learning (AutoML)
Organizers: Charles Weill, Katharina Eggensperger, Matthias Feurer, Frank Hutter, Marius Lindauer, Joaquin Vanschoren

Federated Learning for User Privacy and Data Confidentiality
Keynote: Brendan McMahan
Program Committee: Peter Kairouz, Jakub Konecný

MLRetrospectives: A Venue for Self-Reflection in ML Research
Speaker: Margaret Mitchell

Machine Learning for Media Discovery
Speaker: Ed Chi

INNF+: Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models
Organizers: Chin-Wei Huang, David Krueger, Rianne van den Berg, George Papamakarios, Chris Cremer, Ricky Chen, Danilo Rezende

4th Lifelong Learning Workshop
Program Committee: George Tucker, Marlos C. Machado

2nd ICML Workshop on Human in the Loop Learning (HILL)
Organizers: Shanghang Zhang, Xin Wang, Fisher Yu, Jiajun Wu, Trevor Darrell

Machine Learning for Global Health
Organizers: Danielle Belgrave, Danielle Belgrave, Stephanie Hyland, Charles Onu, Nicholas Furnham, Ernest Mwebaze, Neil Lawrence

Social Chair: Adam White

Work performed while at Google

Source: Google AI Blog

Google at ACL 2020

This week, the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), a premier conference covering a broad spectrum of research areas that are concerned with computational approaches to natural language, takes place online.

As a leader in natural language processing and understanding, and a Diamond Level sponsor of ACL 2020, Google will showcase the latest research in the field with over 30 publications, and the organization of and participation in a variety of workshops and tutorials.

If you’re registered for ACL 2020, we hope that you’ll visit the Google virtual booth to learn more about the projects and opportunities at Google that go into solving interesting problems for billions of people. You can also learn more about the Google research being presented at ACL 2020 below (Google affiliations bolded).

Diversity & Inclusion (D&I) Chair: Vinodkumar Prabhakaran
Accessibility Chair: Sushant Kafle
Local Sponsorship Chair: Kristina Toutanova
Virtual Infrastructure Committee: Yi Luan
Area Chairs: Anders Søgaard, Ankur Parikh, Annie Louis, Bhuvana Ramabhadran, Christo Kirov, Daniel Cer, Dipanjan Das, Diyi Yang, Emily Pitler, Eunsol Choi, George Foster, Idan Szpektor, Jacob Eisenstein, Jason Baldridge, Jun Suzuki, Kenton Lee, Luheng He, Marius Pasca, Ming-Wei Chang, Sebastian Gehrmann, Shashi Narayan, Slav Petrov, Vinodkumar Prabhakaran, Waleed Ammar, William Cohen

Long Papers
Cross-modal Language Generation using Pivot Stabilization for Web-scale Language Coverage
Ashish V. Thapliyal, Radu Soricut

Automatic Detection of Generated Text is Easiest when Humans are Fooled
Daphne Ippolito, Daniel Duckworth, Chris Callison-Burch, Douglas Eck

On Faithfulness and Factuality in Abstractive Summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, Ryan McDonald

MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices
Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, Denny Zhou

BabyWalk: Going Farther in Vision-and-Language Navigation by Taking Baby Steps
Wang Zhu, Hexiang Hu, Jiacheng Chen, Zhiwei Deng, Vihan Jain, Eugene Ie, Fei Sha

Dynamic Programming Encoding for Subword Segmentation in Neural Machine Translation
Xuanli He, Gholamreza Haffari, Mohammad Norouzi

GoEmotions: A Dataset of Fine-Grained Emotions
Dorottya Demszky, Dana Movshovitz-Attias, Jeongwoo Ko, Alan Cowen, Gaurav Nemade, Sujith Ravi

TaPas: Weakly Supervised Table Parsing via Pre-training (see blog post)
Jonathan Herzig, Pawel Krzysztof Nowak, Thomas Müller, Francesco Piccinno, Julian Eisenschlos

Toxicity Detection: Does Context Really Matter?
John Pavlopoulos, Jeffrey Sorensen, Lucas Dixon, Nithum Thain, Ion Androutsopoulos

(Re)construing Meaning in NLP
Sean Trott, Tiago Timponi Torrent, Nancy Chang, Nathan Schneider

Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models
Dan Iter, Kelvin Guu, Larry Lansing, Dan Jurafsky

Probabilistic Assumptions Matter: Improved Models for Distantly-Supervised Document-Level Question Answering
Hao Cheng, Ming-Wei Chang, Kenton Lee, Kristina Toutanova

AdvAug: Robust Adversarial Augmentation for Neural Machine Translation
Yong Cheng, Lu Jiang, Wolfgang Macherey, Jacob Eisenstein

Named Entity Recognition as Dependency Parsing
Juntao Yu, Bernd Bohnet, Massimo Poesio

Cross-modal Coherence Modeling for Caption Generation
Malihe Alikhani, Piyush Sharma, Shengjie Li, Radu Soricut, Matthew Stone

Representation Learning for Information Extraction from Form-like Documents (see blog post)
Bodhisattwa Prasad Majumder, Navneet Potti, Sandeep Tata, James Bradley Wendt, Qi Zhao, Marc Najork

Low-Dimensional Hyperbolic Knowledge Graph Embeddings
Ines Chami, Adva Wolf, Da-Cheng Juan, Frederic Sala, Sujith Ravi, Christopher Ré

What Question Answering can Learn from Trivia Nerds
Jordan Boyd-Graber, Benjamin Börschinger

Learning a Multi-Domain Curriculum for Neural Machine Translation
Wei Wang, Ye Tian, Jiquan Ngiam, Yinfei Yang, Isaac Caswell, Zarana Parekh

Translationese as a Language in "Multilingual" NMT
Parker Riley, Isaac Caswell, Markus Freitag, David Grangier

Mapping Natural Language Instructions to Mobile UI Action Sequences
Yang Li, Jiacong He, Xin Zhou, Yuan Zhang, Jason Baldridge

BLEURT: Learning Robust Metrics for Text Generation (see blog post)
Thibault Sellam, Dipanjan Das, Ankur Parikh

Exploring Unexplored Generalization Challenges for Cross-Database Semantic Parsing
Alane Suhr, Ming-Wei Chang, Peter Shaw, Kenton Lee

Frugal Paradigm Completion
Alexander Erdmann, Tom Kenter, Markus Becker, Christian Schallhart

Short Papers
Reverse Engineering Configurations of Neural Text Generation Models
Yi Tay, Dara Bahri, Che Zheng, Clifford Brunk, Donald Metzler, Andrew Tomkins

Syntactic Data Augmentation Increases Robustness to Inference Heuristics
Junghyun Min, R. Thomas McCoy, Dipanjan Das, Emily Pitler, Tal Linzen

Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation
Aditya Siddhant, Ankur Bapna, Yuan Cao, Orhan Firat, Mia Chen, Sneha Kudugunta, Naveen Arivazhagan, Yonghui Wu

Social Biases in NLP Models as Barriers for Persons with Disabilities
Ben Hutchinson, Vinodkumar Prabhakaran, Emily Denton, Kellie Webster, Yu Zhong, Stephen Denuyl

Toward Better Storylines with Sentence-Level Language Models
Daphne Ippolito, David Grangier, Douglas Eck, Chris Callison-Burch

TACL Papers
TYDI QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages (see blog post)
Jonathan H. Clark, Eunsol Choi, Michael Collins, Dan Garrette, Tom Kwiatkowski, Vitaly Nikolaev, Jennimaria Palomaki

Phonotactic Complexity and Its Trade-offs
Tiago Pimentel, Brian Roark, Ryan Cotterell

Multilingual Universal Sentence Encoder for Semantic Retrieval (see blog post)
Yinfei Yang, Daniel Cer, Amin Ahmad, Mandy Guo, Jax Law, Noah Constant, Gustavo Hernandez Abrego, Steve Yuan, Chris Tar, Yun-Hsuan Sung, Brian Strope, Ray Kurzweil

IWPT - The 16th International Conference on Parsing Technologies
Yuji Matsumoto, Stephan Oepen, Kenji Sagae, Anders Søgaard, Weiwei Sun and Reut Tsarfaty

ALVR - Workshop on Advances in Language and Vision Research
Xin Wang, Jesse Thomason, Ronghang Hu, Xinlei Chen, Peter Anderson, Qi Wu, Asli Celikyilmaz, Jason Baldridge and William Yang Wang

WNGT - The 4th Workshop on Neural Generation and Translation
Alexandra Birch, Graham Neubig, Andrew Finch, Hiroaki Hayashi, Kenneth Heafield, Ioannis Konstas, Yusuke Oda and Xian Li

NLPMC - NLP for Medical Conversations
Parminder Bhatia, Chaitanya Shivade, Mona Diab, Byron Wallace, Rashmi Gangadharaiah, Nan Du, Izhak Shafran and Steven Lin

AutoSimTrans - The 1st Workshop on Automatic Simultaneous Translation
Hua Wu, Colin Cherry, James Cross, Liang Huang, Zhongjun He, Mark Liberman and Yang Liu

Interpretability and Analysis in Neural NLP (cutting-edge)
Yonatan Belinkov, Sebastian Gehrmann, Ellie Pavlick

Commonsense Reasoning for Natural Language Processing (Introductory)
Maarten Sap, Vered Shwartz, Antoine Bosselut, Yejin Choi, Dan Roth

Source: Google AI Blog

Google at CVPR 2020

This week marks the start of the fully virtual 2020 Conference on Computer Vision and Pattern Recognition (CVPR 2020), the premier annual computer vision event consisting of the main conference, workshops and tutorials. As a leader in computer vision research and a Supporter Level Virtual Sponsor, Google will have a strong presence at CVPR 2020, with nearly 70 publications accepted, along with the organization of, and participation in, multiple workshops/tutorials.

If you are participating in CVPR this year, please visit our virtual booth to learn about what Google is actively pursuing for the next generation of intelligent systems that utilize the latest machine learning techniques applied to various areas of machine perception.

You can also learn more about our research being presented at CVPR 2020 in the list below (Google affiliations are bolded).

Organizing Committee

General Chairs: Terry Boult, Gerard Medioni, Ramin Zabih
Program Chairs: Ce Liu, Greg Mori, Kate Saenko, Silvio Savarese
Workshop Chairs: Tal Hassner, Tali Dekel
Website Chairs: Tianfan Xue, Tian Lan
Technical Chair: Daniel Vlasic
Area Chairs include: Alexander Toshev, Alexey Dosovitskiy, Boqing Gong, Caroline Pantofaru, Chen Sun, Deqing Sun, Dilip Krishnan, Feng Yang, Liang-Chieh Chen, Michael Rubinstein, Rodrigo Benenson, Timnit Gebru, Thomas Funkhouser, Varun Jampani, Vittorio Ferrari, William Freeman

Oral Presentations

Evolving Losses for Unsupervised Video Representation Learning
AJ Piergiovanni, Anelia Angelova, Michael Ryoo

CvxNet: Learnable Convex Decomposition
Boyang Deng, Kyle Genova, Soroosh Yazdani, Sofien Bouaziz, Geoffrey Hinton, Andrea Tagliasacchi

Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise
Xuanqing Liu, Tesi Xiao, Si Si, Qin Cao, Sanjiv Kumar, Cho-Jui Hsieh

Scalability in Perception for Autonomous Driving: Waymo Open Dataset
Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla‎, Aurélien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, Vijay Vasudevan, Wei Han, Jiquan Ngiam, Hang Zhao, Aleksei Timofeev‎, Scott Ettinger, Maxim Krivokon, Amy Gao, Aditya Joshi‎, Sheng Zhao, Shuyang Chen, Yu Zhang, Jon Shlens, Zhifeng Chen, Dragomir Anguelov

Deep Implicit Volume Compression
Saurabh Singh, Danhang Tang, Cem Keskin, Philip Chou, Christian Haene, Mingsong Dou, Sean Fanello, Jonathan Taylor, Andrea Tagliasacchi, Philip Davidson, Yinda Zhang, Onur Guleryuz, Shahram Izadi, Sofien Bouaziz

Neural Networks Are More Productive Teachers Than Human Raters: Active Mixup for Data-Efficient Knowledge Distillation from a Blackbox Model
Dongdong Wan, Yandong Li, Liqiang Wang, and Boqing Gong

Google Landmarks Dataset v2 - A Large-Scale Benchmark for Instance-Level Recognition and Retrieval (see the blog post)
Tobias Weyand, Andre Araujo, Jack Sim, Bingyi Cao

CycleISP: Real Image Restoration via Improved Data Synthesis
Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, Ling Shao

Dynamic Graph Message Passing Networks
Li Zhang, Dan Xu, Anurag Arnab, Philip Torr

Local Deep Implicit Functions for 3D Shape
Kyle Genova, Forrester Cole, Avneesh Sud, Aaron Sarna, Thomas Funkhouser

GHUM & GHUML: Generative 3D Human Shape and Articulated Pose Models
Hongyi Xu, Eduard Gabriel Bazavan, Andrei Zanfir, William Freeman, Rahul Sukthankar, Cristian Sminchisescu

Search to Distill: Pearls are Everywhere but not the Eyes
Yu Liu, Xuhui Jia, Mingxing Tan, Raviteja Vemulapalli, Yukun Zhu, Bradley Green, Xiaogang Wang

Semantic Pyramid for Image Generation
Assaf Shocher, Yossi Gandelsman, Inbar Mosseri, Michal Yarom, Michal Irani, William Freeman, Tali Dekel

Flow Contrastive Estimation of Energy-Based Models
Ruiqi Gao, Erik Nijkamp, Diederik Kingma, Zhen Xu, Andrew Dai, Ying Nian Wu

Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from A Domain Adaptation Perspective
Muhammad Abdullah Jamal, Matthew Brown, Ming-Hsuan Yang, Liqiang Wang, Boqing Gong

Category-Level Articulated Object Pose Estimation
Xiaolong Li, He Wang, Li Yi, Leonidas Guibas, Amos Abbott, Shuran Song

AdaCoSeg: Adaptive Shape Co-Segmentation with Group Consistency Loss
Chenyang Zhu, Kai Xu, Siddhartha Chaudhuri, Li Yi, Leonidas Guibas, Hao Zhang

SpeedNet: Learning the Speediness in Videos
Sagie Benaim, Ariel Ephrat, Oran Lang, Inbar Mosseri, William Freeman, Michael Rubinstein, Michal Irani, Tali Dekel

BSP-Net: Generating Compact Meshes via Binary Space Partitioning
Zhiqin Chen, Andrea Tagliasacchi, Hao Zhang

SAPIEN: A SimulAted Part-based Interactive ENvironment
Fanbo Xiang, Yuzhe Qin, Kaichun Mo, Yikuan Xia, Hao Zhu, Fangchen Liu, Minghua Liu, Hanxiao Jiang, Yifu Yuan, He Wang, Li Yi, Angel Chang, Leonidas Guibas, Hao Su

SurfelGAN: Synthesizing Realistic Sensor Data for Autonomous Driving
Zhenpei Yang, Yuning Chai, Dragomir Anguelov, Yin Zhou, Pei Sun, Dumitru Erhan, Sean Rafferty, Henrik Kretzschmar

Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural Networks
Saurabh Singh, Shankar Krishnan

RL-CycleGAN: Reinforcement Learning Aware Simulation-To-Real
Kanishka Rao, Chris Harris, Alex Irpan, Sergey Levine, Julian Ibarz, Mohi Khansari

Open Compound Domain Adaptation
Ziwei Liu, Zhongqi Miao, Xingang Pan, Xiaohang Zhan, Dahua Lin, Stella X.Yu, and Boqing Gong

Single-view view synthesis with multiplane images
Richard Tucker, Noah Snavely

Adversarial Examples Improve Image Recognition
Cihang Xie, Mingxing Tan, Boqing Gong, Jiang Wang, Alan Yuille, Quoc V. Le

Adversarial Texture Optimization from RGB-D Scans
Jingwei Huang, Justus Thies, Angela Dai, Abhijit Kundu, Chiyu “Max” Jiang,Leonidas Guibas, Matthias Niessner, Thomas Funkhouser

Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline
Yu-Lun Liu, Wei-Sheng Lai, Yu-Sheng Chen, Yi-Lung Kao, Ming-Hsuan Yang,Yung-Yu Chuang, Jia-Bin Huang

Collaborative Distillation for Ultra-Resolution Universal Style Transfer
Huan Wang, Yijun Li, Yuehai Wang, Haoji Hu, Ming-Hsuan Yang

Learning to Autofocus
Charles Herrmann, Richard Strong Bowen, Neal Wadhwa, Rahul Garg, Qiurui He, Jonathan T. Barron, Ramin Zabih

Multi-Scale Boosted Dehazing Network with Dense Feature Fusion
Hang Dong, Jinshan Pan, Lei Xiang, Zhe Hu, Xinyi Zhang, Fei Wang, Ming-Hsuan Yang

Composing Good Shots by Exploiting Mutual Relations
Debang Li, Junge Zhang, Kaiqi Huang, Ming-Hsuan Yang

PatchVAE: Learning Local Latent Codes for Recognition
Kamal Gupta, Saurabh Singh, Abhinav Shrivastava

Neural Voxel Renderer: Learning an Accurate and Controllable Rendering Tool
Konstantinos Rematas, Vittorio Ferrari

Local Implicit Grid Representations for 3D Scenes
Chiyu “Max” Jiang, Avneesh Sud, Ameesh Makadia, Jingwei Huang, Matthias Niessner, Thomas Funkhouser

Large Scale Video Representation Learning via Relational Graph Clustering
Hyodong Lee, Joonseok Lee, Joe Yue-Hei Ng, Apostol (Paul) Natsev

Deep Homography Estimation for Dynamic Scenes
Hoang Le, Feng Liu, Shu Zhang, Aseem Agarwala

C-Flow: Conditional Generative Flow Models for Images and 3D Point Clouds
Albert Pumarola, Stefan Popov, Francesc Moreno-Noguer, Vittorio Ferrari

Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination
Pratul Srinivasan, Ben Mildenhall, Matthew Tancik, Jonathan T. Barron, Richard Tucker, Noah Snavely

Scale-space flow for end-to-end optimized video compression
Eirikur Agustsson, David Minnen, Nick Johnston, Johannes Ballé, Sung Jin Hwang, George Toderici

StructEdit: Learning Structural Shape Variations
Kaichun Mo, Paul Guerrero, Li Yi, Hao Su, Peter Wonka, Niloy Mitra, Leonidas Guibas

3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Niessner

Sequential mastery of multiple tasks: Networks naturally learn to learn and forget to forget
Guy Davidson, Michael C. Mozer

Distilling Effective Supervision from Severe Label Noise
Zizhao Zhang, Han Zhang, Sercan Ö. Arik, Honglak Lee, Tomas Pfister

ViewAL: Active Learning With Viewpoint Entropy for Semantic Segmentation
Yawar Siddiqui, Julien Valentin, Matthias Niessner

Attribution in Scale and Space
Shawn Xu, Subhashini Venugopalan, Mukund Sundararajan

Weakly-Supervised Semantic Segmentation via Sub-category Exploration
Yu-Ting Chang, Qiaosong Wang, Wei-Chih Hung, Robinson Piramuthu, Yi-Hsuan Tsai, Ming-Hsuan Yang

Speech2Action: Cross-modal Supervision for Action Recognition
Arsha Nagrani, Chen Sun, David Ross, Rahul Sukthankar, Cordelia Schmid, Andrew Zisserman

Counting Out Time: Class Agnostic Video Repetition Counting in the Wild
Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, Andrew Zisserman

The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction
Junwei Liang, Lu Jiang, Kevin Murphy, Ting Yu, Alexander Hauptmann

Self-training with Noisy Student improves ImageNet classification
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le

EfficientDet: Scalable and Efficient Object Detection (see the blog post)
Mingxing Tan, Ruoming Pang, Quoc Le

ACNe: Attentive Context Normalization for Robust Permutation-Equivariant Learning
Weiwei Sun, Wei Jiang, Eduard Trulls, Andrea Tagliasacchi, Kwang Moo Yi

VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation
Jiyang Gao, Chen Sun, Hang Zhao, Yi Shen, Dragomir Anguelov, Cordelia Schmid, Congcong Li

SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
Xianzhi Du, Tsung-Yi Lin, Pengchong Jin, Golnaz Ghiasi, Mingxing Tan, Yin Cui, Quoc Le, Xiaodan Song

KeyPose: Multi-View 3D Labeling and Keypoint Estimation for Transparent Objects
Xingyu Liu, Rico Jonschkowski, Anelia Angelova, Kurt Konolige

Structured Multi-Hashing for Model Compression
Elad Eban, Yair Movshovitz-Attias, Hao Wu, Mark Sandler, Andrew Poon, Yerlan Idelbayev, Miguel A. Carreira-Perpinan

DOPS: Learning to Detect 3D Objects and Predict their 3D Shapes
Mahyar Najibi, Guangda Lai, Abhijit Kundu, Zhichao Lu, Vivek Rathod, Tom Funkhouser, Caroline Pantofaru, David Ross, Larry Davis, Alireza Fathi

Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation
Bowen Cheng, Maxwell Collins, Yukun Zhu, Ting Liu, Thomas S. Huang, Hartwig Adam, Liang-Chieh Chen

Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection
Sara Beery, Guanhang Wu, Vivek Rathod, Ronny Votel, Jonathan Huang

Distortion Agnostic Deep Watermarking
Xiyang Luo, Ruohan Zhan, Huiwen Chang, Feng Yang, Peyman Milanfar

Can weight sharing outperform random architecture search? An investigation with TuNAS
Gabriel Bender, Hanxiao Liu, Bo Chen, Grace Chu, Shuyang Cheng, Pieter-Jan Kindermans, Quoc Le

GIFnets: Differentiable GIF Encoding Framework
Innfarn Yoo, Xiyang Luo, Yilin Wang, Feng Yang, Peyman Milanfar

Your Local GAN: Designing Two Dimensional Local Attention Mechanisms for Generative Models
Giannis Daras, Augustus Odena, Han Zhang, Alex Dimakis

Fast Sparse ConvNets
Erich Elsen, Marat Dukhan, Trevor Gale, Karen Simonyan

RetinaTrack: Online Single Stage Joint Detection and Tracking
Zhichao Lu, Vivek Rathod, Ronny Votel, Jonathan Huang

Learning to See Through Obstructions
Yu-Lun Liu, Wei-Sheng Lai, Ming-Hsuan Yang,Yung-Yu Chuang, Jia-Bin Huang

Self-Supervised Learning of Video-Induced Visual Invariances
Michael Tschannen, Josip Djolonga, Marvin Ritter, Aravindh Mahendran, Neil Houlsby, Sylvain Gelly, Mario Lucic


3rd Workshop and Challenge on Learned Image Compression
Organizers include: George Toderici, Eirikur Agustsson, Lucas Theis, Johannes Ballé, Nick Johnston

CLVISION 1st Workshop on Continual Learning in Computer Vision
Organizers include: Zhiyuan (Brett) Chen, Marc Pickett

Embodied AI
Organizers include: Alexander Toshev, Jie Tan, Aleksandra Faust, Anelia Angelova

The 1st International Workshop and Prize Challenge on Agriculture-Vision: Challenges & Opportunities for Computer Vision in Agriculture
Organizers include: Zhen Li, Jim Yuan

Embodied AI
Organizers include: Alexander Toshev, Jie Tan, Aleksandra Faust, Anelia Angelova

New Trends in Image Restoration and Enhancement workshop and challenges on image and video restoration and enhancement (NTIRE)
Talk: “Sky Optimization: Semantically aware image processing of skies in low-light photography”
Orly Liba, Longqi Cai, Yun-Ta Tsai, Elad Eban, Yair Movshovitz-Attias, Yael Pritch, Huizhong Chen, Jonathan Barron

The End-of-End-to-End A Video Understanding Pentathlon
Organizers include: Rahul Sukthankar

4th Workshop on Media Forensics
Organizers include: Christoph Bregler

4th Workshop on Visual Understanding by Learning from Web Data
Organizers include: Jesse Berent, Rahul Sukthankar

AI for Content Creation
Organizers include: Deqing Sun, Lu Jiang, Weilong Yang

Fourth Workshop on Computer Vision for AR/VR
Organizers include: Sofien Bouaziz

Low-Power Computer Vision Competition (LPCVC)
Organizers include: Bo Chen, Andrew Howard, Jaeyoun Kim

Sight and Sound
Organizers include: William Freeman

Workshop on Efficient Deep Learning for Computer Vision
Organizers include: Pete Warden

Extreme classification in computer vision
Organizers include: Ramin Zabih, Zhen Li

Image Matching: Local Features and Beyond (see the blog post)
Organizers include: Eduard Trulls

The DAVIS Challenge on Video Object Segmentation
Organizers include: Alberto Montes, Jordi Pont-Tuset, Kevis-Kokitsi Maninis

2nd Workshop on Precognition: Seeing through the Future
Organizers include: Utsav Prabhu

Computational Cameras and Displays (CCD)
Talk: Orly Liba

2nd Workshop on Learning from Unlabeled Videos (LUV)
Organizers include:Honglak Lee, Rahul Sukthankar

7th Workshop on Fine Grained Visual Categorization (FGVC7) (see the blog post)
Organizers include: Christine Kaeser-Chen, Serge Belongie

Language & Vision with applications to Video Understanding
Organizers include: Lu Jiang

Neural Architecture Search and Beyond for Representation Learning
Organizers include: Barret Zoph


Disentangled 3D Representations for Relightable Performance Capture of Humans
Organizers include: Sean Fanello, Christoph Rhemann, Jonathan Taylor, Sofien Bouaziz, Adarsh Kowdle, Rohit Pandey, Sergio Orts-Escolano, Paul Debevec, Shahram Izadi

Learning Representations via Graph-Structured Networks
Organizers include:Chen Sun, Ming-Hsuan Yang

Novel View Synthesis: From Depth-Based Warping to Multi-Plane Images and Beyond
Organizers include:Varun Jampani

How to Write a Good Review
Talks by:Vittorio Ferrari, Bill Freeman, Jordi Pont-Tuset

Neural Rendering
Organizers include:Ricardo Martin-Brualla, Rohit K. Pandey, Sean Fanello,Maneesh Agrawala, Dan B. Goldman

Fairness Accountability Transparency and Ethics and Computer Vision
Organizers: Timnit Gebru, Emily Denton

Source: Google AI Blog

Google at ICLR 2020

This week marks the beginning of the 8th International Conference on Learning Representations (ICLR 2020), a fully virtual conference focused on how one can learn meaningful and useful representations of data for machine learning. ICLR offers conference and workshop tracks, both of which include 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.

As a Diamond Sponsor of ICLR 2020, Google will have a strong virtual presence with over 80 publications accepted, in addition to participating on organizing committees and in workshops. If you have registered for ICLR 20202, we hope you'll watch our talks and learn 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 2020 in the list below (Googlers highlighted in blue).

Officers and Board Members
Includes: Hugo LaRochelle, Samy Bengio, Tara Sainath

Organizing Committee
Includes: Kevin Swersky, Timnit Gebru

Area Chairs
Includes: Balaji Lakshminarayanan, Been Kim, Chelsea Finn, Dale Schuurmans, George Tucker, Honglak Lee, Hossein Mobahi, Jasper Snoek, Justin Gilmer, Katherine Heller, Manaal Faruqui, Michael Ryoo, Nicolas Le Roux, Sanmi Koyejo, Sergey Levine, Tara Sainath, Yann Dauphin, Anders Søgaard, David Duvenaud, Jamie Morgenstern, Qiang Liu

SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference (see the blog post)
Lasse Espeholt, Raphaël Marinier, Piotr Stanczyk, Ke Wang, Marcin Michalski‎

Differentiable Reasoning Over a Virtual Knowledge Base
Bhuwan Dhingra, Manzil Zaheer, Vidhisha Balachandran, Graham Neubig, Ruslan Salakhutdinov, William W. Cohen

Dynamics-Aware Unsupervised Discovery of Skills
Archit Sharma, Shixiang Gu, Sergey Levine, Vikash Kumar, Karol Hausman

GenDICE: Generalized Offline Estimation of Stationary Values
Ruiyi Zhang, Bo Dai, Lihong Li, Dale Schuurmans

Mathematical Reasoning in Latent Space
Dennis Lee, Christian Szegedy, Markus N. Rabe, Kshitij Bansal, Sarah M. Loos

Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One
Will Grathwohl, Kuan-Chieh Wang, Jorn-Henrik Jacobsen, David Duvenaud, Kevin Swersky, Mohammad Norouzi

Adjustable Real-time Style Transfer
Mohammad Babaeizadeh, Golnaz Ghiasi

Are Transformers Universal Approximators of Sequence-to-sequence Functions?
Chulhee Yun, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashankc J. Reddi, Sanjiv Kumar

AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures
Michael S. Ryoo, AJ Piergiovanni, Mingxing Tan, Anelia Angelova

AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan

BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning
Yeming Wen, Dustin Tran, Jimmy Ba

Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning (see the blog post)
Ali Mousavi, Lihong Li, Qiang Liu, Dengyong Zhou

Can Gradient Clipping Mitigate Label Noise?
Aditya Krishna Menon, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar

CAQL: Continuous Action Q-Learning
Moonkyung Ryu, Yinlam Chow, Ross Anderson, Christian Tjandraatmadja, Craig Boutilier

Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation
Byung Hoon Ahn, Prannoy Pilligundla, Amir Yazdanbakhsh, Hadi Esmaeilzadeh

Coherent Gradients: An Approach to Understanding Generalization in Gradient Descent-based Optimization
Satrajit Chatterjee

Consistency Regularization for Generative Adversarial Networks
Han Zhang, Zizhao Zhang, Augustus Odena, Honglak Lee

Contrastive Representation Distillation
Yonglong Tian, Dilip Krishnan, Phillip Isola

Deep Audio Priors Emerge from Harmonic Convolutional Networks
Zhoutong Zhang, Yunyun Wang, Chuang Gan, Jiajun Wu, Joshua B. Tenenbaum, Antonio Torralba, William T. Freeman

Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions
Yao Qin, Nicholas Frosst, Sara Sabour, Colin Raffel, Garrison Cottrell, Geoffrey Hinton

Detecting Extrapolation with Local Ensembles
David Madras, James Atwood, Alexander D'Amour

Disentangling Factors of Variations Using Few Labels
Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem

Distance-Based Learning from Errors for Confidence Calibration
Chen Xing, Sercan Ö. Arik, Zizhao Zhang, Tomas Pfister

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators (see the blog post)
Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning

ES-MAML: Simple Hessian-Free Meta Learning (see the blog post)
Xingyou Song, Yuxiang Yang, Krzysztof Choromanski, Aldo Pacchiano, Wenbo Gao, Yunhao Tang

Exploration in Reinforcement Learning with Deep Covering Options
Yuu Jinnai, Jee Won Park, Marlos C. Machado, George Konidaris

Extreme Tensoring for Low-Memory Preconditioning
Xinyi Chen, Naman Agarwal, Elad Hazan, Cyril Zhang, Yi Zhang

Fantastic Generalization Measures and Where to Find Them
Yiding Jiang, Behnam Neyshabur, Hossein Mobahi, Dilip Krishnan, Samy Bengio

Generalization Bounds for Deep Convolutional Neural Networks
Philip M. Long, Hanie Sedghi

Generalized Convolutional Forest Networks for Domain Generalization and Visual Recognition
Jongbin Ryu, GiTaek Kwon, Ming-Hsuan Yang, Jongwoo Lim

Generative Models for Effective ML on Private, Decentralized Datasets
Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, Blaise Aguera y Arcas

Generative Ratio Matching Networks
Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton

Global Relational Models of Source Code
Vincent J. Hellendoorn, Petros Maniatis, Rishabh Singh, Charles Sutton, David Bieber

Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generation
Suraj Nair, Chelsea Finn

Identity Crisis: Memorization and Generalization Under Extreme Overparameterization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Michael C. Mozer, Yoram Singer

Imitation Learning via Off-Policy Distribution Matching
Ilya Kostrikov, Ofir Nachum, Jonathan Tompson

Language GANs Falling Short
Massimo Caccia, Lucas Caccia, William Fedus, Hugo Larochelle, Joëlle Pineau, Laurent Charlin

Large Batch Optimization for Deep Learning: Training BERT in 76 Minutes
Yang You, Jing Li, Sashank Reddi, Jonathan Hseu, Sanjiv Kumar, Srinadh Bhojanapalli, Xiaodan Song, James Demmel, Kurt Keutzer, Cho-Jui Hsieh

Learning Execution through Neural Code Fusion
Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi

Learning Heuristics for Quantified Boolean Formulas through Reinforcement Learning
Gil Lederman, Markus N. Rabe, Edward A. Lee, Sanjit A. Seshia

Learning to Learn by Zeroth-Order Oracle
Yangjun Ruan, Yuanhao Xiong, Sashank Reddi, Sanjiv Kumar, Cho-Jui Hsieh

Learning to Represent Programs with Property Signatures
Augustus Odena, Charles Sutton

MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius
Runtian Zhai, Chen Dan, Di He, Huan Zhang, Boqing Gong, Pradeep Ravikumar, Cho-Jui Hsieh, Liwei Wang

Measuring Compositional Generalization: A Comprehensive Method on Realistic Data
Daniel Keysers, Nathanael Schärli, Nathan Scales, Hylke Buisman, Daniel Furrer, Sergii Kashubin, Nikola Momchev, Danila Sinopalnikov, Lukasz Stafiniak, Tibor Tihon, Dmitry Tsarkov, Xiao Wang, Marc van Zee, Olivier Bousquet

Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies
Sungryull Sohn, Hyunjae Woo, Jongwook Choi, Honglak Lee

Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, Hugo Larochelle

Model-based Reinforcement Learning for Biological Sequence Design
Christof Angermueller, David Dohan, David Belanger, Ramya Deshpande, Kevin Murphy, Lucy Colwell

Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning
Kimin Lee, Kibok Lee, Jinwoo Shin, Honglak Lee

Observational Overfitting in Reinforcement Learning
Xingyou Song, Yiding Jiang, Stephen Tu, Behnam Neyshabur, Yilun Du

On Bonus-based Exploration Methods In The Arcade Learning Environment
Adrien Ali Taiga, William Fedus, Marlos C. Machado, Aaron Courville, Marc G. Bellemare

On Identifiability in Transformers
Gino Brunner, Yang Liu, Damian Pascual, Oliver Richter, Massimiliano Ciaramita, Roger Wattenhofer

On Mutual Information Maximization for Representation Learning
Michael Tschannen, Josip Djolonga, Paul K. Rubenstein, Sylvain Gelly, Mario Lucic

On the Global Convergence of Training Deep Linear ResNets
Difan Zou, Philip M. Long, Quanquan Gu

Phase Transitions for the Information Bottleneck in Representation Learning
Tailin Wu, Ian Fischer

Pre-training Tasks for Embedding-based Large-scale Retrieval
Wei-Cheng Chang, Felix X. Yu, Yin-Wen Chang, Yiming Yang, Sanjiv Kumar

Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control
Nir Levine, Yinlam Chow, Rui Shu, Ang Li, Mohammad Ghavamzadeh, Hung Bui

Provable Benefit of Orthogonal Initialization in Optimizing Deep Linear Networks
Wei Hu, Lechao Xiao, Jeffrey Pennington

Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML
Aniruddh Raghu, Maithra Raghu, Samy Bengio, Oriol Vinyals

Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs
Aditya Paliwal, Felix Gimeno, Vinod Nair, Yujia Li, Miles Lubin, Pushmeet Kohli, Oriol Vinyals

ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring
David Berthelot, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Han Zhang, Colin Raffel, Kihyuk Sohn

Scalable Model Compression by Entropy Penalized Reparameterization
Deniz Oktay, Johannes Ballé, Saurabh Singh, Abhinav Shrivastava

Scalable Neural Methods for Reasoning With a Symbolic Knowledge Base
William W. Cohen, Haitian Sun, R. Alex Hofer, Matthew Siegler

Semi-Supervised Generative Modeling for Controllable Speech Synthesis
Raza Habib, Soroosh Mariooryad, Matt Shannon, Eric Battenberg, RJ Skerry-Ryan, Daisy Stanton, David Kao, Tom Bagby

Span Recovery for Deep Neural Networks with Applications to Input Obfuscation
Rajesh Jayaram, David Woodruff, Qiuyi Zhang

Thieves on Sesame Street! Model Extraction of BERT-based APIs
Kalpesh Krishna, Gaurav Singh Tomar, Ankur P. Parikh, Nicolas Papernot, Mohit Iyyer

Thinking While Moving: Deep Reinforcement Learning with Concurrent Control
Ted Xiao, Eric Jang, Dmitry Kalashnikov, Sergey Levine, Julian Ibarz, Karol Hausman, Alexander Herzog

VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation
Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan, Chelsea Finn, Sergey Levine, Laurent Dinh, Durk Kingma

Watch, Try, Learn: Meta-Learning from Demonstrations and Rewards
Allan Zhou, Eric Jang, Daniel Kappler, Alex Herzog, Mohi Khansari, Paul Wohlhart, Yunfei Bai, Mrinal Kalakrishnan, Sergey Levine, Chelsea Finn

Weakly Supervised Disentanglement with Guarantees
Rui Shu, Yining Chen, Abhishek Kumar, Stefano Ermon, Ben Poole

You Only Train Once: Loss-Conditional Training of Deep Networks
Alexey Dosovitskiy, Josip Djolonga

A Mutual Information Maximization Perspective of Language Representation Learning
Lingpeng Kong, Cyprien de Masson d’Autume, Wang Ling, Lei Yu, Zihang Dai, Dani Yogatama

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations (see the blog post)
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut

Asymptotics of Wide Networks from Feynman Diagrams
Ethan Dyer, Guy Gur-Ari

DDSP: Differentiable Digital Signal Processing
Jesse Engel, Lamtharn Hantrakul, Chenjie Gu, Adam Roberts

Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation
Ziyang Tang, Yihao Feng, Lihong Li, Dengyong Zhou, Qiang Liu

Dream to Control: Learning Behaviors by Latent Imagination (see the blog post)
Danijar Hafner, Timothy Lillicrap, Jimmy Ba, Mohammad Norouzi

Emergent Tool Use From Multi-Agent Autocurricula
Bowen Baker, Ingmar Kanitscheider, Todor Markov, Yi Wu, Glenn Powell, Bob McGrew, Igor Mordatch

Gradientless Descent: High-Dimensional Zeroth-Order Optimization
Daniel Golovin, John Karro, Greg Kochanski, Chansoo Lee, Xingyou Song, Qiuyi (Richard) Zhang

HOPPITY: Learning Graph Transformations to Detect and Fix Bugs in Programs
Elizabeth Dinella, Hanjun Dai, Ziyang Li, Mayur Naik, Le Song, Ke Wang

Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees
Binghong Chen, Bo Dai, Qinjie Lin, Guo Ye, Han Liu, Le Song

Model Based Reinforcement Learning for Atari (see the blog post)
Łukasz Kaiser, Mohammad Babaeizadeh, Piotr Miłos, Błazej Osinski, Roy H. Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, Henryk Michalewski

Neural Symbolic Reader: Scalable Integration of Distributed and Symbolic Representations for Reading Comprehension
Xinyun Chen, Chen Liang, Adams Wei Yu, Denny Zhou, Dawn Song, Quoc V. Le

SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models
Yucen Luo, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, Ricky T. Q. Chen

Measuring the Reliability of Reinforcement Learning Algorithms
Stephanie C.Y. Chan, Samuel Fishman, John Canny, Anoop Korattikara, Sergio Guadarrama

Meta-Learning without Memorization
Mingzhang Yin, George Tucker, Mingyuan Zhou, Sergey Levine, Chelsea Finn

Neural Tangents: Fast and Easy Infinite Neural Networks in Python (see the blog post)
Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A. Alemi, Jascha Sohl-Dickstein, Samuel S. Schoenholz

Scaling Autoregressive Video Models
Dirk Weissenborn, Oscar Täckström, Jakob Uszkoreit

The Intriguing Role of Module Criticality in the Generalization of Deep Networks
Niladri Chatterji, Behnam Neyshabur, Hanie Sedghi

Reformer: The Efficient Transformer (see the blog post)
Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya

Computer Vision for Global Challenges
Organizing Committee: Ernest Mwebaze
Advisory Committee: Timnit Gebru, John Quinn

Practical ML for Developing Countries: Learning under limited/low resource scenarios
Organizing Committee: Nyalleng Moorosi, Timnit Gebru
Program Committee: Pablo Samuel Castro, Samy Bengio
Keynote Speaker: Karmel Allison

Tackling Climate Change with Machine Learning
Organizing Committee: Moustapha Cisse
Co-Organizer: Natasha Jaques
Program Committee: John C. Platt, Kevin McCloskey, Natasha Jaques
Advisor and Panel: John C. Platt

Towards Trustworthy ML: Rethinking Security and Privacy for ML
Organizing Committee: Nicholas Carlini, Nicolas Papernot
Program Committee: Shuang Song

Source: Google AI Blog

Google at NeurIPS 2019

This week, Vancouver hosts the 33rd annual Conference on Neural Information Processing Systems (NeurIPS 2019), 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. As a Diamond Sponsor of NeurIPS 2019, Google will have a strong presence at NeurIPS 2019 with more than 500 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 more than 130 accepted papers.

If you are attending NeurIPS 2019, 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, such as ML-based Flood Forecasting, AI for Social Good, Google Research Football, Google Dataset Search, TF-Agents and much more. You can also learn more about our work being presented in the list below (Google affiliations highlighted in blue).

NeurIPS Foundation Board
Samy Bengio, Corinna Cortes

NeurIPS Advisory Board
John C. Platt, Fernando Pereira, Dale Schuurmans

NeurIPS Program Committee
Program Chair: Hugo Larochelle
Diversity & Inclusion Co-Chair: Katherine Heller
Meetup Chair: Nicolas La Roux
Party Co-Chair: Pablo Samuel Castro

Senior Area Chairs include: Amir Globerson, Claudio Gentile, Cordelia Schmid, Corinna Cortes, Dale Schuurmans, Elad Hazan, Honglak Lee, Mehryar Mohri, Peter Bartlett, Satyen Kale, Sergey Levine, Surya Ganguli

Area Chairs include: Afshin Rostamizadeh, Alex Kulesza, Amin Karbasi, Andrew Dai, Been Kim, Boqing Gong, Brainslav Kveton, Ce Liu, Charles Sutton, Chelsea Finn, Cho-Jui Hsieh, D Sculley, Danny Tarlow, David Held, Denny Zhou, Yann Dauphin, Dustin Tran, Hartmut Neven, Hossein Mobahi, Ilya Tolstikhin, Jasper Snoek, Jean-Philippe Vert, Jeffrey Pennington, Kevin Swersky, Kun Zhang, Kunal Talwar, Lihong Li, Manzil Zaheer, Marc G Bellemare, Marco Cuturi, Maya Gupta, Meg Mitchell, Minmin Chen, Mohammad Norouzi, Moustapha Cisse, Olivier Bachem, Qiang Liu, Rong Ge, Sanjiv Kumar, Sanmi Koyejo, Sebastian Nowozin, Sergei Vassilvitskii, Shivani Agarwal, Slav Petrov, Srinadh Bhojanapalli, Stephen Bach, Timnit Gebru, Tomer Koren, Vitaly Feldman, William Cohen, Yann Dauphin, Nicolas La Roux

NeurIPS Workshops Program Committee
Yann Dauphin, Honglak Lee, Sebastian Nowozin, Fernanda Viegas

NeurIPS Invited Talk
Social Intelligence
Blaise Aguera y Arcas

Accepted Papers
Memory Efficient Adaptive Optimization
Rohan Anil, Vineet Gupta, Tomer Koren, Yoram Singer

Stand-Alone Self-Attention in Vision Models
Niki Parmar, Prajit Ramachandran, Ashish Vaswani, Irwan Bello, Anselm Levskaya, Jon Shlens

High Fidelity Video Prediction with Large Neural Nets
Ruben Villegas, Arkanath Pathak, Harini Kannan, Dumitru Erhan, Quoc V. Le, Honglak Lee

Unsupervised Learning of Object Structure and Dynamics from Videos
Matthias Minderer, Chen Sun, Ruben Villegas, Forrester Cole, Kevin Murphy, Honglak Lee

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Dehao Chen, Mia Chen, Hyouk Joong Lee, Jiquan Ngiam, Quoc V. Le, Yonghui Wu, Zhifeng Chen

Quadratic Video Interpolation
Xiangyu Xu, Li Si-Yao, Wenxiu Sun, Qian Yin, Ming-Hsuan Yang

Online Stochastic Shortest Path with Bandit Feedback and Unknown Transition Function
Aviv Rosenberg, Yishay Mansour

Individual Regret in Cooperative Nonstochastic Multi-Armed Bandits
Yogev Bar-On, Yishay Mansour

Learning to Screen
Alon Cohen, Avinatan Hassidim, Haim Kaplan, Yishay Mansour, Shay Moran

DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections
Ofir Nachum, Yinlam Chow, Bo Dai, Lihong Li

A Kernel Loss for Solving the Bellman Equation
Yihao Feng, Lihong Li, Qiang Liu

Accurate Uncertainty Estimation and Decomposition in Ensemble Learning
Jeremiah Liu, John Paisley, Marithani-Anna Kioumourtzoglou, Brent Coull

Saccader: Improving Accuracy of Hard Attention Models for Vision
Gamaleldin F. Elsayed, Simon Kornblith, Quoc V. Le

Invertible Convolutional Flow
Mahdi Karami, Dale Schuurmans, Jascha Sohl-Dickstein, Laurent Dinh, Daniel Duckworth

Hypothesis Set Stability and Generalization
Dylan J. Foster, Spencer Greenberg, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan

Bandits with Feedback Graphs and Switching Costs
Raman Arora, Teodor V. Marinov, Mehryar Mohri

Regularized Gradient Boosting
Corinna Cortes, Mehryar Mohri, Dmitry Storcheus

Logarithmic Regret for Online Control
Naman Agarwal, Elad Hazan, Karan Singh

Sampled Softmax with Random Fourier Features
Ankit Singh Rawat, Jiecao Chen, Felix Yu, Ananda Theertha Suresh, Sanjiv Kumar

Multilabel Reductions: What is My Loss Optimising?
Aditya Krishna Menon, Ankit Singh Rawat, Sashank Reddi, Sanjiv Kumar

MetaInit: Initializing Learning by Learning to Initialize
Yann N. Dauphin, Sam Schoenholz

Generalization Bounds for Neural Networks via Approximate Description Length
Amit Daniely, Elad Granot

Variance Reduction of Bipartite Experiments through Correlation Clustering
Jean Pouget-Abadie, Kevin Aydin, Warren Schudy, Kay Brodersen, Vahab Mirrokni

Likelihood Ratios for Out-of-Distribution Detection
Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan

Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
Yaniv Ovadia, Emily Fertig, Jie Jessie Ren, D. Sculley, Josh Dillon, Sebastian Nowozin, Zack Nado, Balaji Lakshminarayanan, Jasper Snoek

Surrogate Objectives for Batch Policy Optimization in One-step Decision Making
Minmin Chen, Ramki Gummadi, Chris Harris, Dale Schuurmans

Globally Optimal Learning for Structured Elliptical Losses
Yoav Wald, Nofar Noy, Gal Elidan, Ami Wiesel

DPPNet: Approximating Determinantal Point Processes with Deep Networks
Zelda Mariet, Yaniv Ovadia, Jasper Snoek

Graph Normalizing Flows
Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, Kevin Swersky

When Does Label Smoothing Help?
Rafael Muller, Simon Kornblith, Geoff Hinton

On the Role of Inductive Bias From Simulation and the Transfer to the Real World: a new Disentanglement Dataset
Muhammad Waleed Gondal, Manuel Wüthrich, Đorđe Miladinović, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer

On the Fairness of Disentangled Representations
Francesco Locatello, Gabriele Abbati, Tom Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem

Are Disentangled Representations Helpful for Abstract Visual Reasoning?
Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, Olivier Bachem

Don’t Blame the ELBO! A Linear VAE Perspective on Posterior Collapse
James Lucas, George Tucker, Roger Grosse, Mohammad Norouzi

Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction
Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, Sergey Levine

Optimizing Generalized Rate Metrics with Game Equilibrium
Harikrishna Narasimhan, Andrew Cotter, Maya Gupta

On Making Stochastic Classifiers Deterministic
Andrew Cotter, Harikrishna Narasimhan, Maya Gupta

Discrete Flows: Invertible Generative Models of Discrete Data
Dustin Tran, Keyon Vafa, Kumar Agrawal, Laurent Dinh, Ben Poole

Graph Agreement Models for Semi-Supervised Learning
Otilia Stretcu, Krishnamurthy Viswanathan, Dana Movshovitz-Attias, Emmanouil Platanios, Andrew Tomkins, Sujith Ravi

A Robust Non-Clairvoyant Dynamic Mechanism for Contextual Auctions
Yuan Deng, Sébastien Lahaie, Vahab Mirrokni

Adversarial Robustness through Local Linearization
Chongli Qin, James Martens, Sven Gowal, Dilip Krishnan, Krishnamurthy (Dj) Dvijotham, Alhusein Fawzi, Soham De, Robert Stanforth, Pushmeet Kohli

A Geometric Perspective on Optimal Representations for Reinforcement Learning
Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taiga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle

Online Learning via the Differential Privacy Lens
Jacob Abernethy, Young Hun Jung, Chansoo Lee, Audra McMillan, Ambuj Tewari

Reducing the Variance in Online Optimization by Transporting Past Gradients
Sébastien M. R. Arnold, Pierre-Antoine Manzagol, Reza Babanezhad, Ioannis Mitliagkas, Nicolas Le Roux

Universality and Individuality in Neural Dynamics Across Large Populations of Recurrent Networks
Niru Maheswaranathan, Alex Williams, Matt Golub, Surya Ganguli, David Sussillo

Reverse Engineering Recurrent Networks for Sentiment Classification Reveals Line Attractor Dynamics
Niru Maheswaranathan, Alex H. Williams, Matthew D. Golub, Surya Ganguli, David Sussillo

Strategizing Against No-Regret Learners
Yuan Deng, Jon Schneider, Balasubramanian Sivan

Prior-Free Dynamic Auctions with Low Regret Buyers
Yuan Deng, Jon Schneider, Balasubramanian Sivan

Private Stochastic Convex Optimization with Optimal Rates
Raef Bassily, Vitaly Feldman, Kunal Talwar, Abhradeep Thakurta

Computational Separations between Sampling and Optimization
Kunal Talwar

Momentum-Based Variance Reduction in Non-Convex SGD
Ashok Cutkosky and Francesco Orabona

Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration
Kwang-Sung Jun, Ashok Cutkosky, Francesco Orabona

Fast and Flexible Multi-Task Classification using Conditional Neural Adaptive Processes
James Requeima, Jonathan Gordon, John Bronskill, Sebastian Nowozin, Richard E. Turner

Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model
Wenbo Gong, Sebastian Tschiatschek, Richard E. Turner, Sebastian Nowozin, Jose Miguel Hernandez-Lobato, Cheng Zhang

Multiview Aggregation for Learning Category-Specific Shape Reconstruction
Srinath Sridhar, Davis Rempe, Julien Valentin, Sofien Bouaziz, Leonidas J. Guibas

Visualizing and Measuring the Geometry of BERT
Andy Coenen, Emily Reif, Ann Yuan, Been Kim, Adam Pearce, Fernanda Viégas, Martin Wattenberg

Locality-Sensitive Hashing for f-Divergences: Mutual Information Loss and Beyond
Lin Chen, Hossein Esfandiari, Thomas Fu, Vahab S. Mirrokni

A Benchmark for Interpretability Methods in Deep Neural Networks
Sara Hooker, Dumitru Erhan, Pieter-jan Kindermans, Been Kim

Practical and Consistent Estimation of f-Divergences
Paul Rubenstein, Olivier Bousquet, Josip Djolonga, Carlos Riquelme, Ilya Tolstikhin

Tree-Sliced Variants of Wasserstein Distances
Tam Le, Makoto Yamada, Kenji Fukumizu, Marco Cuturi

Game Design for Eliciting Distinguishable Behavior
Fan Yang, Liu Leqi, Yifan Wu, Zachary Lipton, Pradeep Ravikumar, Tom M Mitchell, William Cohen

Differentially Private Anonymized Histograms
Ananda Theertha Suresh

Locally Private Gaussian Estimation
Matthew Joseph, Janardhan Kulkarni, Jieming Mao, Zhiwei Steven Wu

Exponential Family Estimation via Adversarial Dynamics Embedding
Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, Dale Schuurmans

Learning to Predict Without Looking Ahead: World Models Without Forward Prediction
C. Daniel Freeman, Luke Metz, David Ha

Adaptive Density Estimation for Generative Models
Thomas Lucas, Konstantin Shmelkov, Karteek Alahari, Cordelia Schmid, Jakob Verbeek

Weight Agnostic Neural Networks
Adam Gaier, David Ha

Retrosynthesis Prediction with Conditional Graph Logic Network
Hanjun Dai, Chengtao Li, Connor Coley, Bo Dai, Le Song

Large Scale Structure of Neural Network Loss Landscapes
Stanislav Fort, Stainslaw Jastrzebski

Off-Policy Evaluation via Off-Policy Classification
Alex Irpan, Kanishka Rao, Konstantinos Bousmalis, Chris Harris, Julian Ibarz, Sergey Levine

Domes to Drones: Self-Supervised Active Triangulation for 3D Human Pose Reconstruction
Aleksis Pirinen, Erik Gartner, Cristian Sminchisescu

Energy-Inspired Models: Learning with Sampler-Induced Distributions
Dieterich Lawson, George TuckerBo Dai, Rajesh Ranganath

From Deep Learning to Mechanistic Understanding in Neuroscience: The Structure of Retinal Prediction
Hidenori Tanaka, Aran Nayebi, Niru Maheswaranathan, Lane McIntosh, Stephen Baccus, Surya Ganguli

Language as an Abstraction for Hierarchical Deep Reinforcement Learning
Yiding Jiang, Shixiang Gu, Kevin Murphy, Chelsea Finn

Bayesian Layers: A Module for Neural Network Uncertainty
Dustin Tran, Michael W. Dusenberry, Mark van der Wilk, Danijar Hafner

Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates
Hugo Penedones, Carlos RiquelmeDamien Vincent, Hartmut Maennel, Timothy Mann, Andre Barreto, Sylvain Gelly, Gergely Neu

A Unified Framework for Data Poisoning Attack to Graph-based Semi-Supervised Learning
Xuanqing Liu, Si Si, Xiaojin Zhu, Yang Li, Cho-Jui Hsieh

MixMatch: A Holistic Approach to Semi-Supervised Learning
David Berthelot, Nicholas Carlini, Ian Goodfellow (work done while at Google), Avital Oliver, Nicolas Papernot, Colin Raffel

SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies
Seyed Kamyar Seyed Ghasemipour, Shixiang (Shane) Gu, Richard Zemel

Limits of Private Learning with Access to Public Data
Noga Alon, Raef Bassily, Shay Moran

Regularized Weighted Low Rank Approximation
Frank Ban, David Woodruff, Richard Zhang

Unsupervised Curricula for Visual Meta-Reinforcement Learning
Allan Jabri, Kyle Hsu, Abhishek Gupta, Benjamin Eysenbach, Sergey Levine, Chelsea Finn

Secretary Ranking with Minimal Inversions
Sepehr Assadi, Eric Balkanski, Renato Paes Leme

Mixtape: Breaking the Softmax Bottleneck Efficiently
Zhilin Yang, Thang Luong, Russ Salakhutdinov, Quoc V. Le

Budgeted Reinforcement Learning in Continuous State Space
Nicolas Carrara, Edouard Leurent, Romain Laroche, Tanguy Urvoy, Odalric-Ambrym Maillard, Olivier Pietquin

From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization
Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang

Generalization Bounds for Neural Networks via Approximate Description Length
Amit Daniely, Elad Granot

Flattening a Hierarchical Clustering through Active Learning
Fabio Vitale, Anand Rajagopalan, Claudio Gentile

Robust Attribution Regularization
Jiefeng Chen, Xi Wu, Vaibhav Rastogi, Yingyu Liang, Somesh Jha

Robustness Verification of Tree-based Models
Hongge Chen, Huan Zhang, Si Si, Yang Li, Duane Boning, Cho-Jui Hsieh

Meta Architecture Search
Albert Shaw, Wei Wei, Weiyang Liu, Le Song, Bo Dai

Contextual Bandits with Cross-Learning
Santiago Balseiro, Negin Golrezaei, Mohammad Mahdian, Vahab Mirrokni, Jon Schneider

Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions
Negin Golrezaei, Adel Javanmard, Vahab Mirrokni

Optimizing Generalized Rate Metrics with Three Players
Harikrishna Narasimhan, Andrew Cotter, Maya Gupta

Noise-Tolerant Fair Classification
Alexandre Louis Lamy, Ziyuan Zhong, Aditya Krishna Menon, Nakul Verma

Towards Automatic Concept-based Explanations
Amirata Ghorbani, James Wexler, James Zou, Been Kim

Locally Private Learning without Interaction Requires Separation
Amit Daniely, Vitaly Feldman

Learning GANs and Ensembles Using Discrepancy
Ben Adlam, Corinna Cortes, Mehryar Mohri, Ningshan Zhang

CondConv: Conditionally Parameterized Convolutions for Efficient Inference
Brandon Yang, Gabriel Bender, Quoc V. Le, Jiquan Ngiam

A Fourier Perspective on Model Robustness in Computer Vision
Dong Yin, Raphael Gontijo Lopes, Jonathon Shlens, Ekin D. Cubuk, Justin Gilmer

Robust Bi-Tempered Logistic Loss Based on Bregman Divergences
Ehsan Amid, Manfred K. Warmuth, Rohan Anil, Tomer Koren

When Does Label Smoothing Help?
Rafael Müller, Simon Kornblith, Geoffrey Hinton

Memory Efficient Adaptive Optimization
Rohan Anil, Vineet Gupta, Tomer Koren, Yoram Singer

Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model
Guodong Zhang, Lala Li, Zachary Nado, James Martens, Sushant Sachdeva, George E. Dahl, Christopher J. Shallue, Roger Grosse

Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent
Jaehoon Lee, Lechao Xiao, Samuel S. Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, Jeffrey Pennington

Universality and Individuality in Neural Dynamics Across Large Populations of Recurrent Networks
Niru Maheswaranathan, Alex H. Williams, Matthew D. Golub, Surya Ganguli, David Sussillo

Abstract Reasoning with Distracting Features
Kecheng Zheng, Zheng-Jun Zha, Wei Wei

Search on the Replay Buffer: Bridging Planning and Reinforcement Learning
Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine

Differentiable Ranking and Sorting Using Optimal Transport
Marco Cuturi, Olivier Teboul, Jean-Philippe Vert

XLNet: Generalized Autoregressive Pretraining for Language Understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le

Private Learning Implies Online Learning: An Efficient Reduction
Alon Gonen, Elad Hazan, Shay Moran

Evaluating Protein Transfer Learning with TAPE
Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Peter Chen, John Canny, Pieter Abbeel, Yun Song

Tight Dimensionality Reduction for Sketching Low Degree Polynomial Kernels
Michela Meister, Tamas Sarlos, David P. Woodruff

No Pressure! Addressing the Problem of Local Minima in Manifold Learning Algorithms
Max Vladymyrov

Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections
Boris Muzellec, Marco Cuturi

Online Stochastic Shortest Path with Bandit Feedback and Unknown Transition Function
Aviv Rosenberg, Yishay Mansour

Private Learning Implies Online Learning: An Efficient Reduction
Alon Gonen, Elad Hazan, Shay Moran

On the Fairness of Disentangled Representations
Francesco Locatello, Gabriele Abbati, Tom Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem

On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset
Muhammad Waleed Gondal, Manuel Wüthrich, Ðorde Miladinovíc, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer

Stacked Capsule Autoencoders
Adam R. Kosiorek, Sara Sabour, Yee Whye Teh, Geoffrey E. Hinton

Wasserstein Dependency Measure for Representation Learning
Sherjil Ozair, Corey Lynch, Yoshua Bengio, Aaron van den Oord, Sergey Levine, Pierre Sermanet

Sampling Sketches for Concave Sublinear Functions of Frequencies
Edith Cohen, Ofir Geri

Hamiltonian Neural Networks
Sam Greydanus, Misko Dzamba, Jason Yosinski

Evaluating Protein Transfer Learning with TAPE
Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Xi Chen, John Canny, Pieter Abbeel, Yun S. Song

Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization
Miika Aittala, Prafull Sharma, Lukas Murmann, Adam B. Yedidia, Gregory W. Wornell, William T. Freeman, Frédo Durand

Quadratic Video Interpolation
Xiangyu Xu, Li Siyao, Wenxiu Sun, Qian Yin, Ming-Hsuan Yang

Transfusion: Understanding Transfer Learning for Medical Imagings
Maithra Raghu, Chiyuan Zhang, Jon Kleinberg, Samy Bengio

XLNet: Generalized Autoregressive Pretraining for Language Understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le

Differentially Private Covariance Estimation
Kareem Amin, Travis Dick, Alex Kulesza, Andres Munoz, Sergei Vassilvitskii

Private Stochastic Convex Optimization with Optimal Rates
Raef Bassily, Vitaly Feldman, Kunal Talwar, Abhradeep Thakurta

Learning Transferable Graph Exploration
Hanjun Dai, Yujia Li, Chenglong Wang, Rishabh Singh, Po-Sen Huang, Pushmeet Kohli

Neural Attribution for Semantic Bug-Localization in Student Programs
Rahul Gupta, Aditya Kanade, Shirish Shevade

PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, Soumith Chintala

Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces
Chuan Guo, Ali Mousavi, Xiang Wu, Daniel Holtmann-Rice, Satyen Kale, Sashank Reddi, Sanjiv Kumar

Efficient Rematerialization for Deep Networks
Ravi Kumar, Manish Purohit, Zoya Svitkina, Erik Vee, Joshua R. Wang

Momentum-Based Variance Reduction in Non-Convex SGD
Ashok Cutkosky, Francesco Orabona

Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration
Kwang-Sung Jun, Ashok Cutkosky, Francesco Orabona

3rd Conversational AI: Today's Practice and Tomorrow's Potential
Organizers include: Bill Byrne

AI for Humanitarian Assistance and Disaster Response Workshop
Invited Speakers include: Yossi Matias

Bayesian Deep Learning
Organizers include: Kevin P Murphy

Beyond First Order Methods in Machine Learning Systems
Invited Speakers include: Elad Hazan

Biological and Artificial Reinforcement Learning
Invited Speakers include: Igor Mordatch

Context and Compositionality in Biological and Artificial Neural Systems
Invited Speakers include: Kenton Lee

Deep Reinforcement Learning
Organizers include: Chelsea Finn

Document Intelligence
Organizers include: Tania Bedrax Weiss

Federated Learning for Data Privacy and Confidentiality
Organizers include: Jakub KonečnýBrendan McMahan
Invited Speakers include: Françoise Beaufays, Daniel Ramage

Graph Representation Learning
Organizers include: Rianne van den Berg

Human-Centric Machine Learning
Invited Speakers include: Been Kim

Information Theory and Machine Learning
Organizers include: Ben Poole
Invited Speakers include: Alex Alemi

KR2ML - Knowledge Representation and Reasoning Meets Machine Learning
Invited Speakers include: William Cohen

Learning Meaningful Representations of Life
Organizers include: Jasper Snoek, Alexander Wiltschko

Learning Transferable Skills
Invited Speakers include: David Ha

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

Machine Learning for Health (ML4H): What Makes Machine Learning in Medicine Different?
Invited Speakers include: Lily Peng, Alan Karthikesalingam, Dale Webster

Machine Learning and the Physical Sciences
Speakers include: Yasaman Bahri, Samual Schoenholz

ML for Systems
Organizers include: Milad HashemiKevin SwerskyAzalia MirhoseiniAnna Goldie
Invited Speakers include: Jeff Dean

Optimal Transport for Machine Learning
Organizers include: Marco Cuturi

The Optimization Foundations of Reinforcement Learning
Organizers include: Bo DaiNicolas Le RouxLihong LiDale Schuurmans

Privacy in Machine Learning
Invited Speakers include: Brendan McMahan

Program Transformations for ML
Organizers include: Pascal LamblinAlexander WiltschkoBart van MerrienboerEmily Fertig
Invited Speakers include: Skye Wanderman-Milne

Real Neurons & Hidden Units: Future Directions at the Intersection of Neuroscience and Artificial Intelligence
Organizers include: David Sussillo

Robot Learning: Control and Interaction in the Real World
Organizers include: Stefan Schaal

Safety and Robustness in Decision Making
Organizers include: Yinlam Chow

Science Meets Engineering of Deep Learning
Invited Speakers include: Yasaman Bahri, Surya Ganguli‎, Been Kim, Surya Ganguli

Sets and Partitions
Organizers include: Manzil Zaheer, Andrew McCallum
Invited Speakers include: Amr Ahmed

Tackling Climate Change with ML
Organizers include: John Platt
Invited Speakers include: Jeff Dean

Visually Grounded Interaction and Language
Invited Speakers include: Jason Baldridge

Workshop on Machine Learning with Guarantees
Invited Speakers include: Mehryar Mohri

Representation Learning and Fairness
Organizers include: Moustapha Cisse, Sanmi Koyejo

Source: Google AI Blog

Building Skills, Building Community

Year after year, we hear from conference attendees that it's not just the content they came for, it's the connections. Meeting new people, getting new perspectives, making new friends (and sometimes hiring them!) is a big part of KubeCon Life. We want to make sure that the Kubecon community is welcoming to people from diverse backgrounds but just being welcoming is not enough: we have to actually do the work to help people get through the door.

The easiest way to help people get through the door is through diversity scholarships. One of the biggest blockers to full participation in our community is just having the resources to get to the room where it happens, and a diversity scholarship—not just a ticket, but travel assistance too—helps increase participation.

1: Going Swagless

This Kubecon we want you to take away the really important things from the conference: new knowledge and new connections... not just another pen or plastic doodad. (Although to be fair, we will also have plenty of stickers... stickers aren't swag, they're an essential part of Kubecon!)

Google prides itself on being a data-driven company, so when we need to decide where we can spend our dollars to make the most impact and do the most good for the Kubecon community, we turn to the data. We know there is an issue from the CNCF KubeCon report in Seattle 2018 reporting in 11% women (and that’s not even a complete diversity metric). Now looking at the things conference attendees have told us they value about Kubecon, we put together this handy chart to help us guide our decision-making:
Travel + Conf Ticket ScholarshipBranded Pen
Face to face learning
Career development
OSS community building
Writing tools

We also need to consider externalities when we make our decisions—and going #swagless and dedicating those resources to improving the conversation and community at Kubecon has some positive externalities: less plastic (and lighter luggage going home) is better for the planet, too!

If our work to support diversity and inclusion at Kubecon has inspired you and you want to know what your org can do to participate, there is plenty of room in the #swagless tent for everyone—redirect your swag budget to D&I efforts. Shoutouts to conference organizers like SpringOne that went totally swagless this year!

2: Diversity Lunch + Hack

Our commitment to a welcoming environment and a diverse community doesn't stop at getting people in the door: we also need to work on inclusion. Our diversity lunch and hack is a place where people can:
  • Build their skills through pair programming
  • Get installation help
  • Do deep-dives on k8s topics
  • Connect with others in the community
Our diversity lunch isn't just talking about diversity: it's about working towards diversity through skill-building and creating stronger community bonds. Register here!

We welcomed 220 friends and allies in Barcelona and expect to continue the sold-out streak in San Diego (get your ticket now)!

3: Redirecting Even More

But wait, there's more! We're not just going #swagless, we're also redirecting all the hands-on workshop registration fees ($50) from Anthos Day, Anthos&GKE Lab, OSS: Agones, Knative, and Kubeflow to the diversity scholarship fund. You can build a stronger, more diverse community while you build your skills—a total twofer. (And our workshops are also walking the walk of inclusion by being accessible themselves: if you need support to attend a workshop, whether financial or physical, send us a note.

4: Hiring

Also, one of the best things any company can do to drive D&I is to hire people who will help your company become more diverse, whether as a consultant to help you build your program, or as a team member who will help you bring a wider perspective to your product! Come meet a Googler at any of the activities we are doing during the week to discuss jobs at Google Cloud: g.co/Kubecon.

By: Paris Pittman, Google Open Source

DevFest 2019: It’s time for Latin America!

DevFest banner Posted by Mariela Altamirano, Community Manager for Latin America with Grant Timmerman, Developer Programs Engineer and Mete Atamel, Developer Advocate

DevFest season is always full of lively surprises with enchanting adventures right around the corner. Sometimes these adventures are big: attending a DevFest in the Caribbean, in the heart of the amazon jungle, or traveling more than 3,000 meters above sea level to discover the beautiful South American highlands. Other times they are small but precious: unlocking a new way of thinking that completely shifts how you code.

October marks the beginning of our DevFest 2019 season in Latin America, where all of these experiences become a reality thanks to the efforts of our communities.

What makes DevFests in LATAM different? Our community is free spirited, eager to explore the natural landscapes we call home, proud of our deep cultural diversity, and energized by our big cities. At the same time, we are connected to the tranquil spirit of our small towns. This year, we hope to reflect this way of life through our 55 official Latin America DevFests.

During the season, Latin America will open its doors to Google Developer Experts, Women Techmakers, Googlers, and other renowned speakers, to exchange ideas on Google products such as Android, TensorFlow, Flutter, Google Cloud Platform. Activities include, hackathons, codelabs and training sessions. This season, we will be joined by Googlers Grant Timmerman and Mete Atamel.

Grant is a Developer Programs Engineer at Google where he works on Cloud Functions, Cloud Run, and other serverless technologies on Google Cloud Platform. He loves open source, Node, and plays the alto saxophone in his spare time. During his time in Latin America, he'll be discussing all things serverless at DevFests and Cloud Summits in Chile, Argentina, Peru, Colombia, and Mexico.

Grant Timmerman, developer programs engineer
Mete Atamel, developer advocate

Mete is a Developer Advocate based in London. He focuses on helping developers with Google Cloud. At DevFest Sul in Floripa and other conferences and meetups throughout Brazil in October, he’ll be talking about serverless containers using Knative and Cloud Run. He first visited the region back in 2017 when he visited Sao Paulo

Afterwards, he went to Rio de Janeiro and immediately fell in love with the city, its friendly people and its positive vibe. Since then, he spoke at a number of conferences and meetups in Mexico, Colombia, Peru, Argentina, Uruguay and Brazil, and always has been impressed with the eagerness of people to learn more.

This year we will be visiting new countries such as Jamaica, Haiti, Guyana, Honduras, Venezuela and Ecuador that have created their first GDG (Google Developer Group) communities. Most of these new communities are celebrating their first DevFest! We'll also be hosting diversity and inclusion events, so keep an eye out for more details!

We thank everyone for being a part of DevFest and our community.

We hope you join us!



Find a DevFest near you at g.co/dev/fest/sa

Google at Interspeech 2019

This week, Graz, Austria hosts the 20th Annual Conference of the International Speech Communication Association (Interspeech 2019), one of the world‘s most extensive conferences on the research and engineering for spoken language processing. Over 2,000 experts in speech-related research fields gather to take part in oral presentations and poster sessions and to collaborate with streamed events across the globe.

As a Gold Sponsor of Interspeech 2019, we are excited to present 30 research publications, and demonstrate some of the impact speech technology has made in our products, from accessible, automatic video captioning to a more robust, reliable Google Assistant. If you’re attending Interspeech 2019, we hope that you’ll stop by the Google booth to meet our researchers and discuss projects and opportunities at Google that go into solving interesting problems for billions of people. Our researchers will also be on hand to discuss Google Cloud Text-to-Speech and Speech-to-text, demo Parrotron, and more. You can also learn more about the Google research being presented at Interspeech 2019 below (Google affiliations in blue).

Organizing Committee includes:
Michiel Bacchiani

Technical Program Committee includes:
Tara Sainath

Neural Machine Translation
Organizers include: Wolfgang Macherey, Yuan Cao

Accepted Publications
Building Large-Vocabulary ASR Systems for Languages Without Any Audio Training Data (link to appear soon)
Manasa Prasad, Daan van Esch, Sandy Ritchie, Jonas Fromseier Mortensen

Multi-Microphone Adaptive Noise Cancellation for Robust Hotword Detection (link to appear soon)
Yiteng Huang, Turaj Shabestary, Alexander Gruenstein, Li Wan

Direct Speech-to-Speech Translation with a Sequence-to-Sequence Model
Ye Jia, Ron Weiss, Fadi Biadsy, Wolfgang Macherey, Melvin Johnson, Zhifeng Chen, Yonghui Wu

Improving Keyword Spotting and Language Identification via Neural Architecture Search at Scale (link to appear soon)
Hanna Mazzawi, Javier Gonzalvo, Aleks Kracun, Prashant Sridhar, Niranjan Subrahmanya, Ignacio Lopez Moreno, Hyun Jin Park, Patrick Violette

Shallow-Fusion End-to-End Contextual Biasing (link to appear soon)
Ding Zhao, Tara Sainath, David Rybach, Pat Rondon, Deepti Bhatia, Bo Li, Ruoming Pang

VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking
Quan Wang, Hannah Muckenhirn, Kevin Wilson, Prashant Sridhar, Zelin Wu, John Hershey, Rif Saurous, Ron Weiss, Ye Jia, Ignacio Lopez Moreno

SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
Daniel Park, William Chan, Yu Zhang, Chung-Cheng Chiu, Barret Zoph, Ekin Dogus Cubuk, Quoc Le

Two-Pass End-to-End Speech Recognition
Ruoming Pang, Tara Sainath, David Rybach, Yanzhang He, Rohit Prabhavalkar, Mirko Visontai, Qiao Liang, Trevor Strohman, Yonghui Wu, Ian McGraw, Chung-Cheng Chiu

On the Choice of Modeling Unit for Sequence-to-Sequence Speech Recognition
Kazuki Irie, Rohit Prabhavalkar, Anjuli Kannan, Antoine Bruguier, David Rybach, Patrick Nguyen

Contextual Recovery of Out-of-Lattice Named Entities in Automatic Speech Recognition (link to appear soon)
Jack Serrino, Leonid Velikovich, Petar Aleksic, Cyril Allauzen

Joint Speech Recognition and Speaker Diarization via Sequence Transduction
Laurent El Shafey, Hagen Soltau, Izhak Shafran

Personalizing ASR for Dysarthric and Accented Speech with Limited Data
Joel Shor, Dotan Emanuel, Oran Lang, Omry Tuval, Michael Brenner, Julie Cattiau, Fernando Vieira, Maeve McNally, Taylor Charbonneau, Melissa Nollstadt, Avinatan Hassidim, Yossi Matias

An Investigation Into On-Device Personalization of End-to-End Automatic Speech Recognition Models (link to appear soon)
Khe Chai Sim, Petr Zadrazil, Francoise Beaufays

Salient Speech Representations Based on Cloned Networks
Bastiaan Kleijn, Felicia Lim, Michael Chinen, Jan Skoglund

Cross-Lingual Consistency of Phonological Features: An Empirical Study (link to appear soon)
Cibu Johny, Alexander Gutkin, Martin Jansche

LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech
Heiga Zen, Viet Dang, Robert Clark, Yu Zhang, Ron Weiss, Ye Jia, Zhifeng Chen, Yonghui Wu

Improving Performance of End-to-End ASR on Numeric Sequences
Cal Peyser, Hao Zhang, Tara Sainath, Zelin Wu

Developing Pronunciation Models in New Languages Faster by Exploiting Common Grapheme-to-Phoneme Correspondences Across Languages (link to appear soon)
Harry Bleyan, Sandy Ritchie, Jonas Fromseier Mortensen, Daan van Esch

Phoneme-Based Contextualization for Cross-Lingual Speech Recognition in End-to-End Models
Ke Hu, Antoine Bruguier, Tara Sainath, Rohit Prabhavalkar, Golan Pundak

Fréchet Audio Distance: A Reference-free Metric for Evaluating Music Enhancement Algorithms
Kevin Kilgour, Mauricio Zuluaga, Dominik Roblek, Matthew Sharifi

Learning to Speak Fluently in a Foreign Language: Multilingual Speech Synthesis and Cross-Language Voice Cloning
Yu Zhang, Ron Weiss, Heiga Zen, Yonghui Wu, Zhifeng Chen, RJ Skerry-Ryan, Ye Jia, Andrew Rosenberg, Bhuvana Ramabhadran

Sampling from Stochastic Finite Automata with Applications to CTC Decoding
Martin Jansche, Alexander Gutkin

Large-Scale Multilingual Speech Recognition with a Streaming End-to-End Model (link to appear soon)
Anjuli Kannan, Arindrima Datta, Tara Sainath, Eugene Weinstein, Bhuvana Ramabhadran, Yonghui Wu, Ankur Bapna, Zhifeng Chen, SeungJi Lee

A Real-Time Wideband Neural Vocoder at 1.6 kb/s Using LPCNet
Jean-Marc Valin, Jan Skoglund

Low-Dimensional Bottleneck Features for On-Device Continuous Speech Recognition
David Ramsay, Kevin Kilgour, Dominik Roblek, Matthew Sharif

Unified Verbalization for Speech Recognition & Synthesis Across Languages (link to appear soon)
Sandy Ritchie, Richard Sproat, Kyle Gorman, Daan van Esch, Christian Schallhart, Nikos Bampounis, Benoit Brard, Jonas Mortensen, Amelia Holt, Eoin Mahon

Better Morphology Prediction for Better Speech Systems (link to appear soon)
Dravyansh Sharma, Melissa Wilson, Antoine Bruguier

Dual Encoder Classifier Models as Constraints in Neural Text Normalization
Ajda Gokcen, Hao Zhang, Richard Sproat

Large-Scale Visual Speech Recognition
Brendan Shillingford, Yannis Assael, Matthew Hoffman, Thomas Paine, Cían Hughes, Utsav Prabhu, Hank Liao, Hasim Sak, Kanishka Rao, Lorrayne Bennett, Marie Mulville, Ben Coppin, Ben Laurie, Andrew Senior, Nando de Freitas

Parrotron: An End-to-End Speech-to-Speech Conversion Model and its Applications to Hearing-Impaired Speech and Speech Separation
Fadi Biadsy, Ron Weiss, Pedro Moreno, Dimitri Kanevsky, Ye Jia

Source: Google AI Blog

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

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

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

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