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Google at ICLR 2021

The 9th International Conference on Learning Representations (ICLR 2021), a virtual conference focused on deep learning, kicked off this week, offering conference and workshop tracks that present some of the latest research in deep learning and its applications to areas such as computer vision, computational biology, speech recognition, text understanding, and more.

As a Platinum Sponsor of ICLR 2021, Google will have a strong presence with over 100 accepted publications and participation on organizing committees and in workshops. If you have registered for ICLR 2021, we hope you’ll watch our talks and learn about the work at Google that goes into solving interesting problems for billions of people. Learn more about our research being presented in the list below (Googlers in bold).

Officers and Board Members
Includes: Hugo Larochelle, Tara Sainath

Organizing Committee
Includes: Sanmi Koyejo, Chelsea Finn

Area Chairs
Includes: Abhishek Kumar, Aditya Menon, Aleksandra Faust, Alexey Dosovitskiy, Andrew Cotter, Andrew Dai, Augustus Odena, Been Kim, Behnam Neyshabur, Ben Poole, Bo Dai, Bo Li, Branislav Kveton, Ce Liu, Claudio Gentile, Colin Raffel, Danny Tarlow, David Ha, Dengyong Zhou, Dumitru Erhan, Dustin Tran, Felix Hill, George Tucker, Hanie Sedghi, Heinrich Jiang, Hossein Mobahi, Izhak Shafran, Jascha Sohl-Dickstein, Jasper Snoek, Jean-Philippe Vert, Jeffrey Pennington, Justin Gilmer, Kevin Swersky, Marco Cuturi, Mario Lucic, Marlos C. Machado, Mathieu Blondel, Matt Johnson, Matthieu Geist, Mohammad Norouzi, Naman Agarwal, Navdeep Jaitly, Nicolas Le Roux, Niki Parmar, Olivier Bachem, Olivier Pietquin, Philip Long, Quentin Berthet, Razvan Pascanu, Rodolphe Jenatton, Samy Bengio*, Sebastian Nowozin, Silvio Lattanzi, Slav Petrov, Srinadh Bhojanapalli, Suman Ravuri, Tim Salimans, Vitaly Kuznetsov, William Cohen, Yann Dauphin, Yujia Li

Publications
Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes
Mike Gartrell, Insu Han, Elvis Dohmatob, Jennifer Gillenwater, Victor-Emmanuel Brunel

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (see the blog post)
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby

Share or Not? Learning to Schedule Language-Specific Capacity for Multilingual Translation
Biao Zhang*, Ankur Bapna, Rico Sennrich, Orhan Firat

Evolving Reinforcement Learning Algorithms (see the blog post)
John D Co-Reyes, Yingjie Miao, Daiyi Peng, Esteban Real, Quoc V Le, Sergey Levine, Honglak Lee, Aleksandra Faust

Score-Based Generative Modeling through Stochastic Differential Equations
Yang Song*, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole

What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study
Marcin Andrychowicz, Anton Raichuk, Piotr Stańczyk, Manu Orsini, Sertan Girgin, Raphaël Marinier, Leonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, Sylvain Gelly, Olivier Bachem

When Do Curricula Work?
Xiaoxia Wu, Ethan Dyer, Behnam Neyshabur

Sharpness-aware Minimization for Efficiently Improving Generalization
Pierre Foret*, Ariel Kleiner, Hossein Mobahi, Behnam Neyshabur

Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models Zirui Wang*, Yulia Tsvetkov, Orhan Firat, Yuan Cao

Mathematical Reasoning via Self-supervised Skip-tree Training
Markus Norman Rabe, Dennis Lee, Kshitij Bansal, Christian Szegedy

Long-Tail Learning via Logit Adjustment
Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, Sanjiv Kumar

Are Neural Rankers Still Outperformed by Gradient Boosted Decision Trees?
Zhen Qin, Le Yan, Honglei Zhuang, Yi Tay, Rama Kumar Pasumarthi, Xuanhui Wang, Michael Bendersky, Marc Najork

LambdaNetworks: Modeling Long-Range Interactions without Attention
Irwan Bello

Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning
Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G Bellemare

BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration
Augustus Odena, Kensen Shi, David Bieber, Rishabh Singh, Charles Sutton, Hanjun Dai

Practical Real Time Recurrent Learning with a Sparse Approximation
Jacob Menick, Erich Elsen, Utku Evci, Simon Osindero, Karen Simonyan, Alex Graves

LEAF: A Learnable Frontend for Audio Classification (see the blog post)
Neil Zeghidour, Olivier Teboul, Félix de Chaumont Quitry, Marco Tagliasacchi

Batch Reinforcement Learning Through Continuation Method
Yijie Guo, Shengyu Feng, Nicolas Le Roux, Ed Chi, Honglak Lee, Minmin Chen

Scalable Transfer Learning with Expert Models
Joan Puigcerver, Carlos Riquelme Ruiz, Basil Mustafa, Cedric Renggli*, André Susano Pinto, Sylvain Gelly, Daniel Keysers, Neil Houlsby

Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning
Rishabh Agarwal, Marlos C. Machado*, Pablo Samuel Castro, Marc G Bellemare

Scaling Symbolic Methods Using Gradients for Neural Model Explanation
Subham Sekhar Sahoo, Subhashini Venugopalan, Li Li, Rishabh Singh, Patrick Riley

Primal Wasserstein Imitation Learning (see the blog post)
Robert Dadashi, Leonard Hussenot, Matthieu Geist, Olivier Pietquin

Reset-Free Lifelong Learning with Skill-Space Planning
Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch

Teaching Temporal Logics to Neural Networks
Christopher Hahn, Frederik Schmitt, Jens U. Kreber, Markus Norman Rabe, Bernd Finkbeiner

Shape-Texture Debiased Neural Network Training
Yingwei Li, Qihang Yu, Mingxing Tan, Jieru Mei, Peng Tang, Wei Shen, Alan Yuille, Cihang Xie

Rethinking Embedding Coupling in Pre-trained Language Models
Hyung Won Chung, Thibault Fevry*, Henry Tsai, Melvin Johnson, Sebastian Ruder

Overparameterisation and Worst-Case Generalisation: Friend or Foe?
Aditya Krishna Menon, Ankit Singh Rawat, Sanjiv Kumar

Single-Photon Image Classification
Thomas Fischbacher, Luciano Sbaiz

Into the Wild with AudioScope: Unsupervised Audio-Visual Separation of On-Screen Sounds
Efthymios Tzinis*, Scott Wisdom, Aren Jansen, Shawn Hershey, Tal Remez, Daniel P. W. Ellis, John R. Hershey

Adaptive Federated Optimization
Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, Hugh Brendan McMahan

Share or Not? Learning to Schedule Language-Specific Capacity for Multilingual Translation
Biao Zhang*, Ankur Bapna, Rico Sennrich, Orhan Firat

Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers
Benjamin Eysenbach, Shreyas Chaudhari, Swapnil Asawa, Sergey Levine, Ruslan Salakhutdinov

Open Question Answering over Tables and Text
Wenhu Chen*, Ming-Wei Chang, Eva Schlinger, William Yang Wang, William W. Cohen

Practical Real Time Recurrent Learning with a Sparse Approximation
Jacob Menick, Erich Elsen, Utku Evci, Simon Osindero, Karen Simonyan, Alex Graves

IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression
Rianne van den Berg, Alexey A. Gritsenko, Mostafa Dehghani, Casper Kaae Sønderby, Tim Salimans

A Universal Representation Transformer Layer for Few-Shot Image Classification
Lu Liu, William L. Hamilton, Guodong Long, Jing Jiang, Hugo Larochelle

Tradeoffs in Data Augmentation: An Empirical Study
Raphael Gontijo-Lopes, Sylvia Smullin, Ekin Dogus Cubuk, Ethan Dyer

Coping with Label Shift via Distributionally Robust Optimisation
Jingzhao Zhang, Aditya Krishna Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, Suvrit Sra

Rethinking Attention with Performers (see the blog post)
Krzysztof Marcin Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Quincy Davis, Afroz Mohiuddin, Lukasz Kaiser, David Benjamin Belanger, Lucy J Colwell, Adrian Weller

Teaching with Commentaries
Aniruddh Raghu*, Maithra Raghu, Simon Kornblith, David Duvenaud, Geoffrey Hinton

Anatomy of Catastrophic Forgetting: Hidden Representations and Task Semantics
Vinay Venkatesh Ramasesh, Ethan Dyer, Maithra Raghu

Model-Based Offline Planning
Arthur Argenson, Gabriel Dulac-Arnold

The Geometry of Integration in Text Classification RNNs
Kyle Aitken*, Vinay Venkatesh Ramasesh, Ankush Garg, Yuan Cao, David Sussillo, Niru Maheswaranathan

On the Origin of Implicit Regularization in Stochastic Gradient Descent
Samuel L Smith, Benoit Dherin, David Barrett, Soham De

Score-Based Generative Modeling through Stochastic Differential Equations
Yang Song*, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole

The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers (see the blog post)
Preetum Nakkiran*, Behnam Neyshabur, Hanie Sedghi

Learning Energy-Based Models by Diffusion Recovery Likelihood
Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, Diederik P Kingma

Latent Skill Planning for Exploration and Transfer
Kevin Xie, Homanga Bharadhwaj, Danijar Hafner, Animesh Garg, Florian Shkurti

PseudoSeg: Designing Pseudo Labels for Semantic Segmentation
Yuliang Zou*, Zizhao Zhang, Han Zhang, Chun-Liang Li, Xiao Bian, Jia-Bin Huang, Tomas Pfister

WaveGrad: Estimating Gradients for Waveform Generation
Nanxin Chen*, Yu Zhang, Heiga Zen, Ron J Weiss, Mohammad Norouzi, William Chan

One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks
Atish Agarwala, Abhimanyu Das, Brendan Juba*, Rina Panigrahy, Vatsal Sharan*, Xin Wang, Qiuyi Zhang

Long Range Arena : A Benchmark for Efficient Transformers
Yi Tay, Mostafa Dehghani, Samira Abnar, Yikang Shen, Dara Bahri, Philip Pham, Jinfeng Rao, Liu Yang, Sebastian Ruder, Donald Metzler

Explainable Deep One-Class Classification
Philipp Liznerski, Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Marius Kloft, Klaus Robert Muller

Net-DNF: Effective Deep Modeling of Tabular Data
Liran Katzir, Gal Elidan, Ran El-Yaniv

Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization
Tatsuya Matsushima, Hiroki Furuta, Yutaka Matsuo, Ofir Nachum, Shixiang Gu

Auxiliary Task Update Decomposition: The Good, the Bad and the Neutral
Lucio M. Dery, Yann Dauphin, David Grangier

Long-Tail Learning via Logit Adjustment
Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, Sanjiv Kumar

Average-Case Acceleration for Bilinear Games and Normal Matrices
Carles Domingo-Enrich, Fabian Pedregosa, Damien Scieur

OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning
Anurag Ajay*, Aviral Kumar, Pulkit Agrawal, Sergey Levine, Ofir Nachum

Training Independent Subnetworks for Robust Prediction
Marton Havasi*, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew Mingbo Dai, Dustin Tran

Benchmarks for Deep Off-Policy Evaluation
Justin Fu, Mohammad Norouzi, Ofir Nachum, George Tucker, Ziyu Wang, Alexander Novikov, Mengjiao Yang, Michael R Zhang, Yutian Chen, Aviral Kumar, Cosmin Paduraru, Sergey Levine, Thomas Paine

TropEx: An Algorithm for Extracting Linear Terms in Deep Neural Networks
Martin Trimmel, Henning Petzka, Cristian Sminchisescu

Mastering Atari with Discrete World Models (see the blog post)
Danijar Hafner, Timothy P Lillicrap, Mohammad Norouzi, Jimmy Ba

Exploring the Uncertainty Properties of Neural Networks’ Implicit Priors in the Infinite-Width Limit
Danijar Hafner, Timothy P Lillicrap, Mohammad Norouzi, Jimmy Ba

Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning
Ben Adlam, Jaehoon Lee, Lechao Xiao, Jeffrey Pennington, Jasper Snoek

Anchor & Transform: Learning Sparse Embeddings for Large Vocabularies
Paul Pu Liang*, Manzil Zaheer, Yuan Wang, Amr Ahmed

Sharpness-Aware Minimization for Efficiently Improving Generalization
Pierre Foret*, Ariel Kleiner, Hossein Mobahi, Behnam Neyshabur

HyperGrid Transformers: Towards A Single Model for Multiple Tasks
Yi Tay, Zhe Zhao, Dara Bahri, Donald Metzler, Da-Cheng Juan

Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms
Maruan Al-Shedivat*, Jennifer Gillenwater, Eric Xing, Afshin Rostamizadeh

BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration
Augustus Odena, Kensen Shi, David Bieber, Rishabh Singh, Charles Sutton, Hanjun Dai

Are Neural Rankers Still Outperformed by Gradient Boosted Decision Trees?
Zhen Qin, Le Yan, Honglei Zhuang, Yi Tay, Rama Kumar Pasumarthi, Xuanhui Wang, Michael Bendersky, Marc Najork

Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth
Thao Nguyen, Maithra Raghu, Simon Kornblith

A Unifying View on Implicit Bias in Training Linear Neural Networks
Chulhee Yun*, Shankar Krishnan, Hossein Mobahi

Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning
Aviral Kumar, Rishabh Agarwal, Dibya Ghosh, Sergey Levine

Mathematical Reasoning via Self-Supervised Skip-Tree Training
Markus Norman Rabe, Dennis Lee, Kshitij Bansal, Christian Szegedy

Lipschitz Recurrent Neural Networks
N. Benjamin Erichson, Omri Azencot, Alejandro Queiruga, Liam Hodgkinson, Michael W. Mahoney

Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization
Michael R Zhang*, Thomas Paine, Ofir Nachum, Cosmin Paduraru, George Tucker, ziyu wang, Mohammad Norouzi

The Importance of Pessimism in Fixed-Dataset Policy Optimization
Jacob Buckman, Carles Gelada, Marc G Bellemare

Monotonic Kronecker-Factored Lattice
William Taylor Bakst, Nobuyuki Morioka, Erez Louidor

What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study
Marcin Andrychowicz, Anton Raichuk, Piotr Stańczyk, Manu Orsini, Sertan Girgin, Raphaël Marinier, Leonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, Sylvain Gelly, Olivier Bachem

Adversarially Guided Actor-Critic
Yannis Flet-Berliac, Johan Ferret, Olivier Pietquin, Philippe Preux, Matthieu Geist

Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes
Mike Gartrell, Insu Han, Elvis Dohmatob, Jennifer Gillenwater, Victor-Emmanuel Brunel

GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding
Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, Zhifeng Chen

Revisiting Hierarchical Approach for Persistent Long-Term Video Prediction
Wonkwang Lee, Whie Jung, Han Zhang, Ting Chen, Jing Yu Koh, Thomas Huang, Hyungsuk Yoon, Honglak Lee*, Seunghoon Hong

Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models
Zirui Wang, Yulia Tsvetkov, Orhan Firat, Yuan Cao

Dataset Meta-Learning from Kernel Ridge-Regression
Timothy Nguyen, Zhourong Chen, Jaehoon Lee

Dual-Mode ASR: Unify and Improve Streaming ASR with Full-Context Modeling
Jiahui Yu, Wei Han, Anmol Gulati, Chung-Cheng Chiu, Bo Li, Tara N Sainath, Yonghui Wu, Ruoming Pang

Implicit Gradient Regularization
David Barrett, Benoit Dherin

Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning
Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G Bellemare

Deconstructing the Regularization of BatchNorm
Yann Dauphin, Ekin Dogus Cubuk

C-Learning: Learning to Achieve Goals via Recursive Classification
Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine

Evolving Reinforcement Learning Algorithms
John D Co-Reyes, Yingjie Miao, Daiyi Peng, Esteban Real, Quoc V Le, Sergey Levine, Honglak Lee, Aleksandra Faust

Colorization Transformer
Manoj Kumar, Dirk Weissenborn, Nal Kalchbrenner

Control-Aware Representations for Model-based Reinforcement Learning
Brandon Cui, Yinlam Chow, Mohammad Ghavamzadeh

Evaluations and Methods for Explanation through Robustness Analysis
Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu, Pradeep Kumar Ravikumar, Seungyeon Kim, Sanjiv Kumar, Cho-Jui Hsieh

Learning and Evaluating Representations for Deep One-Class Classification
Kihyuk Sohn, Chun-Liang Li, Jinsung Yoon, Minho Jin, Tomas Pfister

No MCMC for Me: Amortized Sampling for Fast and Stable Training of Energy-Based Models
Will Sussman Grathwohl, Jacob Jin Kelly, Milad Hashemi, Mohammad Norouzi, Kevin Swersky, David Duvenaud

Neural Thompson Sampling
Weitong ZHANG, Dongruo Zhou, Lihong Li, Quanquan Gu

A Design Space Study for LISTA and Beyond
Tianjian Meng, Xiaohan Chen, Yifan Jiang, Zhangyang Wang

i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning
Kibok Lee, Yian Zhu, Kihyuk Sohn, Chun-Liang Li, Jinwoo Shin, Honglak Lee

Factorizing Declarative and Procedural Knowledge in Structured, Dynamical Environments
Anirudh Goyal, Alex Lamb, Phanideep Gampa, Philippe Beaudoin, Charles Blundell, Sergey Levine, Yoshua Bengio, Michael Curtis Mozer

Calibration of Neural Networks using Splines
Kartik Gupta, Amir Rahimi, Thalaiyasingam Ajanthan, Thomas Mensink, Cristian Sminchisescu, Richard Hartley

Extreme Memorization via Scale of Initialization
Harsh Mehta, Ashok Cutkosky, Behnam Neyshabur

Molecule Optimization by Explainable Evolution
Binghong Chen, Tianzhe Wang, Chengtao Li, Hanjun Dai, Le Song

Combining Ensembles and Data Augmentation Can Harm Your Calibration
Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, Dustin Tran

Workshops
Science and Engineering of Deep Learning
Speakers and Panelists include: Alex Hanna
Moderator and Advisors include: Emily Denton
Organizers include: Negar Rostemzadeh, Samy Bengio*

Synthetic Data Generation: Quality, Privacy, Bias
Speakers include: Jinsung Yoon, Emily Denton
Program Committee includes: Syed Ashrafulla

Enormous Language Models: Perspectives and Benchmarks
Speakers and Panelists include: Noam Shazeer, Natalie Schluter
Organizers include: Colin Raffel, Adam Roberts, Jascha Sohl-Dickstein, Katherine Lee, William Fedus, Aitor Lewkowycz

The Role of Mathematical Reasoning in General Artificial Intelligence
Speakers and Panelists include: Markus Rabe, Christian Szegedy

Weakly Supervised Learning
Invited Speakers include: Lu Jiang

Learning to Learn
Organizers include: Yevgen Chebotar

Embodied Multimodal Learning (EML)
Invited Speakers includes: Sergey Levine

Distributed and Private Machine Learning
Program Committee includes: Peter Kairouz, Ananda Theertha Suresh

S2D-OLAD: From Shallow to Deep, Overcoming Limited and Adverse Data
Invited Speakers include: Alex Hanna, Hugo Larochelle
Organizers include: Vincent Dumoulin

Responsible AI (RAI)
Speakers include: Been Kim

Energy-Based Models: Current Perspectives, Challenges, and Opportunities
Organizers include: Adji Bousso Dieng, Igor Mordatch

A Roadmap to Never-Ending RL
Invited Session Panelists include: Aleksandra Faust
Program Committee includes: Coline Devin, Karol Hausman, Ben Eysenbach, Ofir Nachum, Ryan Julian, Tianhe Yu, Dumitru Erhan, Marc Pickett, Shixiang Gu

2nd Workshop on Practical ML for Developing Countries: Learning Under Limited/low Resource Scenarios
Program Committee includes: Pablo Samuel Castro

Beyond Static Papers: Rethinking How We Share Scientific Understanding in ML
Speakers include: David Ha, Hugo Larochelle
Organizers include: Sara Hooker


* Indicates work done while at Google

Source: Google AI Blog


Google at ICLR 2021

The 9th International Conference on Learning Representations (ICLR 2021), a virtual conference focused on deep learning, kicked off this week, offering conference and workshop tracks that present some of the latest research in deep learning and its applications to areas such as computer vision, computational biology, speech recognition, text understanding, and more.

As a Platinum Sponsor of ICLR 2021, Google will have a strong presence with over 100 accepted publications and participation on organizing committees and in workshops. If you have registered for ICLR 2021, we hope you’ll watch our talks and learn about the work at Google that goes into solving interesting problems for billions of people. Learn more about our research being presented in the list below (Googlers in bold).

Officers and Board Members
Includes: Hugo Larochelle, Tara Sainath

Organizing Committee
Includes: Sanmi Koyejo, Chelsea Finn

Area Chairs
Includes: Abhishek Kumar, Aditya Menon, Aleksandra Faust, Alexey Dosovitskiy, Andrew Cotter, Andrew Dai, Augustus Odena, Been Kim, Behnam Neyshabur, Ben Poole, Bo Dai, Bo Li, Branislav Kveton, Ce Liu, Claudio Gentile, Colin Raffel, Danny Tarlow, David Ha, Dengyong Zhou, Dumitru Erhan, Dustin Tran, Felix Hill, George Tucker, Hanie Sedghi, Heinrich Jiang, Hossein Mobahi, Izhak Shafran, Jascha Sohl-Dickstein, Jasper Snoek, Jean-Philippe Vert, Jeffrey Pennington, Justin Gilmer, Kevin Swersky, Marco Cuturi, Mario Lucic, Marlos C. Machado, Mathieu Blondel, Matt Johnson, Matthieu Geist, Mohammad Norouzi, Naman Agarwal, Navdeep Jaitly, Nicolas Le Roux, Niki Parmar, Olivier Bachem, Olivier Pietquin, Philip Long, Quentin Berthet, Razvan Pascanu, Rodolphe Jenatton, Samy Bengio*, Sebastian Nowozin, Silvio Lattanzi, Slav Petrov, Srinadh Bhojanapalli, Suman Ravuri, Tim Salimans, Vitaly Kuznetsov, William Cohen, Yann Dauphin, Yujia Li

Publications
Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes
Mike Gartrell, Insu Han, Elvis Dohmatob, Jennifer Gillenwater, Victor-Emmanuel Brunel

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (see the blog post)
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby

Share or Not? Learning to Schedule Language-Specific Capacity for Multilingual Translation
Biao Zhang*, Ankur Bapna, Rico Sennrich, Orhan Firat

Evolving Reinforcement Learning Algorithms (see the blog post)
John D Co-Reyes, Yingjie Miao, Daiyi Peng, Esteban Real, Quoc V Le, Sergey Levine, Honglak Lee, Aleksandra Faust

Score-Based Generative Modeling through Stochastic Differential Equations
Yang Song*, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole

What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study
Marcin Andrychowicz, Anton Raichuk, Piotr Stańczyk, Manu Orsini, Sertan Girgin, Raphaël Marinier, Leonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, Sylvain Gelly, Olivier Bachem

When Do Curricula Work?
Xiaoxia Wu, Ethan Dyer, Behnam Neyshabur

Sharpness-aware Minimization for Efficiently Improving Generalization
Pierre Foret*, Ariel Kleiner, Hossein Mobahi, Behnam Neyshabur

Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models Zirui Wang*, Yulia Tsvetkov, Orhan Firat, Yuan Cao

Mathematical Reasoning via Self-supervised Skip-tree Training
Markus Norman Rabe, Dennis Lee, Kshitij Bansal, Christian Szegedy

Long-Tail Learning via Logit Adjustment
Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, Sanjiv Kumar

Are Neural Rankers Still Outperformed by Gradient Boosted Decision Trees?
Zhen Qin, Le Yan, Honglei Zhuang, Yi Tay, Rama Kumar Pasumarthi, Xuanhui Wang, Michael Bendersky, Marc Najork

LambdaNetworks: Modeling Long-Range Interactions without Attention
Irwan Bello

Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning
Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G Bellemare

BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration
Augustus Odena, Kensen Shi, David Bieber, Rishabh Singh, Charles Sutton, Hanjun Dai

Practical Real Time Recurrent Learning with a Sparse Approximation
Jacob Menick, Erich Elsen, Utku Evci, Simon Osindero, Karen Simonyan, Alex Graves

LEAF: A Learnable Frontend for Audio Classification (see the blog post)
Neil Zeghidour, Olivier Teboul, Félix de Chaumont Quitry, Marco Tagliasacchi

Batch Reinforcement Learning Through Continuation Method
Yijie Guo, Shengyu Feng, Nicolas Le Roux, Ed Chi, Honglak Lee, Minmin Chen

Scalable Transfer Learning with Expert Models
Joan Puigcerver, Carlos Riquelme Ruiz, Basil Mustafa, Cedric Renggli*, André Susano Pinto, Sylvain Gelly, Daniel Keysers, Neil Houlsby

Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning
Rishabh Agarwal, Marlos C. Machado*, Pablo Samuel Castro, Marc G Bellemare

Scaling Symbolic Methods Using Gradients for Neural Model Explanation
Subham Sekhar Sahoo, Subhashini Venugopalan, Li Li, Rishabh Singh, Patrick Riley

Primal Wasserstein Imitation Learning (see the blog post)
Robert Dadashi, Leonard Hussenot, Matthieu Geist, Olivier Pietquin

Reset-Free Lifelong Learning with Skill-Space Planning
Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch

Teaching Temporal Logics to Neural Networks
Christopher Hahn, Frederik Schmitt, Jens U. Kreber, Markus Norman Rabe, Bernd Finkbeiner

Shape-Texture Debiased Neural Network Training
Yingwei Li, Qihang Yu, Mingxing Tan, Jieru Mei, Peng Tang, Wei Shen, Alan Yuille, Cihang Xie

Rethinking Embedding Coupling in Pre-trained Language Models
Hyung Won Chung, Thibault Fevry*, Henry Tsai, Melvin Johnson, Sebastian Ruder

Overparameterisation and Worst-Case Generalisation: Friend or Foe?
Aditya Krishna Menon, Ankit Singh Rawat, Sanjiv Kumar

Single-Photon Image Classification
Thomas Fischbacher, Luciano Sbaiz

Into the Wild with AudioScope: Unsupervised Audio-Visual Separation of On-Screen Sounds
Efthymios Tzinis*, Scott Wisdom, Aren Jansen, Shawn Hershey, Tal Remez, Daniel P. W. Ellis, John R. Hershey

Adaptive Federated Optimization
Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, Hugh Brendan McMahan

Share or Not? Learning to Schedule Language-Specific Capacity for Multilingual Translation
Biao Zhang*, Ankur Bapna, Rico Sennrich, Orhan Firat

Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers
Benjamin Eysenbach, Shreyas Chaudhari, Swapnil Asawa, Sergey Levine, Ruslan Salakhutdinov

Open Question Answering over Tables and Text
Wenhu Chen*, Ming-Wei Chang, Eva Schlinger, William Yang Wang, William W. Cohen

Practical Real Time Recurrent Learning with a Sparse Approximation
Jacob Menick, Erich Elsen, Utku Evci, Simon Osindero, Karen Simonyan, Alex Graves

IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression
Rianne van den Berg, Alexey A. Gritsenko, Mostafa Dehghani, Casper Kaae Sønderby, Tim Salimans

A Universal Representation Transformer Layer for Few-Shot Image Classification
Lu Liu, William L. Hamilton, Guodong Long, Jing Jiang, Hugo Larochelle

Tradeoffs in Data Augmentation: An Empirical Study
Raphael Gontijo-Lopes, Sylvia Smullin, Ekin Dogus Cubuk, Ethan Dyer

Coping with Label Shift via Distributionally Robust Optimisation
Jingzhao Zhang, Aditya Krishna Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, Suvrit Sra

Rethinking Attention with Performers (see the blog post)
Krzysztof Marcin Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Quincy Davis, Afroz Mohiuddin, Lukasz Kaiser, David Benjamin Belanger, Lucy J Colwell, Adrian Weller

Teaching with Commentaries
Aniruddh Raghu*, Maithra Raghu, Simon Kornblith, David Duvenaud, Geoffrey Hinton

Anatomy of Catastrophic Forgetting: Hidden Representations and Task Semantics
Vinay Venkatesh Ramasesh, Ethan Dyer, Maithra Raghu

Model-Based Offline Planning
Arthur Argenson, Gabriel Dulac-Arnold

The Geometry of Integration in Text Classification RNNs
Kyle Aitken*, Vinay Venkatesh Ramasesh, Ankush Garg, Yuan Cao, David Sussillo, Niru Maheswaranathan

On the Origin of Implicit Regularization in Stochastic Gradient Descent
Samuel L Smith, Benoit Dherin, David Barrett, Soham De

Score-Based Generative Modeling through Stochastic Differential Equations
Yang Song*, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole

The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers (see the blog post)
Preetum Nakkiran*, Behnam Neyshabur, Hanie Sedghi

Learning Energy-Based Models by Diffusion Recovery Likelihood
Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, Diederik P Kingma

Latent Skill Planning for Exploration and Transfer
Kevin Xie, Homanga Bharadhwaj, Danijar Hafner, Animesh Garg, Florian Shkurti

PseudoSeg: Designing Pseudo Labels for Semantic Segmentation
Yuliang Zou*, Zizhao Zhang, Han Zhang, Chun-Liang Li, Xiao Bian, Jia-Bin Huang, Tomas Pfister

WaveGrad: Estimating Gradients for Waveform Generation
Nanxin Chen*, Yu Zhang, Heiga Zen, Ron J Weiss, Mohammad Norouzi, William Chan

One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks
Atish Agarwala, Abhimanyu Das, Brendan Juba*, Rina Panigrahy, Vatsal Sharan*, Xin Wang, Qiuyi Zhang

Long Range Arena : A Benchmark for Efficient Transformers
Yi Tay, Mostafa Dehghani, Samira Abnar, Yikang Shen, Dara Bahri, Philip Pham, Jinfeng Rao, Liu Yang, Sebastian Ruder, Donald Metzler

Explainable Deep One-Class Classification
Philipp Liznerski, Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Marius Kloft, Klaus Robert Muller

Net-DNF: Effective Deep Modeling of Tabular Data
Liran Katzir, Gal Elidan, Ran El-Yaniv

Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization
Tatsuya Matsushima, Hiroki Furuta, Yutaka Matsuo, Ofir Nachum, Shixiang Gu

Auxiliary Task Update Decomposition: The Good, the Bad and the Neutral
Lucio M. Dery, Yann Dauphin, David Grangier

Long-Tail Learning via Logit Adjustment
Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, Sanjiv Kumar

Average-Case Acceleration for Bilinear Games and Normal Matrices
Carles Domingo-Enrich, Fabian Pedregosa, Damien Scieur

OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning
Anurag Ajay*, Aviral Kumar, Pulkit Agrawal, Sergey Levine, Ofir Nachum

Training Independent Subnetworks for Robust Prediction
Marton Havasi*, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew Mingbo Dai, Dustin Tran

Benchmarks for Deep Off-Policy Evaluation
Justin Fu, Mohammad Norouzi, Ofir Nachum, George Tucker, Ziyu Wang, Alexander Novikov, Mengjiao Yang, Michael R Zhang, Yutian Chen, Aviral Kumar, Cosmin Paduraru, Sergey Levine, Thomas Paine

TropEx: An Algorithm for Extracting Linear Terms in Deep Neural Networks
Martin Trimmel, Henning Petzka, Cristian Sminchisescu

Mastering Atari with Discrete World Models (see the blog post)
Danijar Hafner, Timothy P Lillicrap, Mohammad Norouzi, Jimmy Ba

Exploring the Uncertainty Properties of Neural Networks’ Implicit Priors in the Infinite-Width Limit
Danijar Hafner, Timothy P Lillicrap, Mohammad Norouzi, Jimmy Ba

Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning
Ben Adlam, Jaehoon Lee, Lechao Xiao, Jeffrey Pennington, Jasper Snoek

Anchor & Transform: Learning Sparse Embeddings for Large Vocabularies
Paul Pu Liang*, Manzil Zaheer, Yuan Wang, Amr Ahmed

Sharpness-Aware Minimization for Efficiently Improving Generalization
Pierre Foret*, Ariel Kleiner, Hossein Mobahi, Behnam Neyshabur

HyperGrid Transformers: Towards A Single Model for Multiple Tasks
Yi Tay, Zhe Zhao, Dara Bahri, Donald Metzler, Da-Cheng Juan

Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms
Maruan Al-Shedivat*, Jennifer Gillenwater, Eric Xing, Afshin Rostamizadeh

BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration
Augustus Odena, Kensen Shi, David Bieber, Rishabh Singh, Charles Sutton, Hanjun Dai

Are Neural Rankers Still Outperformed by Gradient Boosted Decision Trees?
Zhen Qin, Le Yan, Honglei Zhuang, Yi Tay, Rama Kumar Pasumarthi, Xuanhui Wang, Michael Bendersky, Marc Najork

Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth
Thao Nguyen, Maithra Raghu, Simon Kornblith

A Unifying View on Implicit Bias in Training Linear Neural Networks
Chulhee Yun*, Shankar Krishnan, Hossein Mobahi

Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning
Aviral Kumar, Rishabh Agarwal, Dibya Ghosh, Sergey Levine

Mathematical Reasoning via Self-Supervised Skip-Tree Training
Markus Norman Rabe, Dennis Lee, Kshitij Bansal, Christian Szegedy

Lipschitz Recurrent Neural Networks
N. Benjamin Erichson, Omri Azencot, Alejandro Queiruga, Liam Hodgkinson, Michael W. Mahoney

Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization
Michael R Zhang*, Thomas Paine, Ofir Nachum, Cosmin Paduraru, George Tucker, ziyu wang, Mohammad Norouzi

The Importance of Pessimism in Fixed-Dataset Policy Optimization
Jacob Buckman, Carles Gelada, Marc G Bellemare

Monotonic Kronecker-Factored Lattice
William Taylor Bakst, Nobuyuki Morioka, Erez Louidor

What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study
Marcin Andrychowicz, Anton Raichuk, Piotr Stańczyk, Manu Orsini, Sertan Girgin, Raphaël Marinier, Leonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, Sylvain Gelly, Olivier Bachem

Adversarially Guided Actor-Critic
Yannis Flet-Berliac, Johan Ferret, Olivier Pietquin, Philippe Preux, Matthieu Geist

Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes
Mike Gartrell, Insu Han, Elvis Dohmatob, Jennifer Gillenwater, Victor-Emmanuel Brunel

GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding
Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, Zhifeng Chen

Revisiting Hierarchical Approach for Persistent Long-Term Video Prediction
Wonkwang Lee, Whie Jung, Han Zhang, Ting Chen, Jing Yu Koh, Thomas Huang, Hyungsuk Yoon, Honglak Lee*, Seunghoon Hong

Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models
Zirui Wang, Yulia Tsvetkov, Orhan Firat, Yuan Cao

Dataset Meta-Learning from Kernel Ridge-Regression
Timothy Nguyen, Zhourong Chen, Jaehoon Lee

Dual-Mode ASR: Unify and Improve Streaming ASR with Full-Context Modeling
Jiahui Yu, Wei Han, Anmol Gulati, Chung-Cheng Chiu, Bo Li, Tara N Sainath, Yonghui Wu, Ruoming Pang

Implicit Gradient Regularization
David Barrett, Benoit Dherin

Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning
Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G Bellemare

Deconstructing the Regularization of BatchNorm
Yann Dauphin, Ekin Dogus Cubuk

C-Learning: Learning to Achieve Goals via Recursive Classification
Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine

Evolving Reinforcement Learning Algorithms
John D Co-Reyes, Yingjie Miao, Daiyi Peng, Esteban Real, Quoc V Le, Sergey Levine, Honglak Lee, Aleksandra Faust

Colorization Transformer
Manoj Kumar, Dirk Weissenborn, Nal Kalchbrenner

Control-Aware Representations for Model-based Reinforcement Learning
Brandon Cui, Yinlam Chow, Mohammad Ghavamzadeh

Evaluations and Methods for Explanation through Robustness Analysis
Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu, Pradeep Kumar Ravikumar, Seungyeon Kim, Sanjiv Kumar, Cho-Jui Hsieh

Learning and Evaluating Representations for Deep One-Class Classification
Kihyuk Sohn, Chun-Liang Li, Jinsung Yoon, Minho Jin, Tomas Pfister

No MCMC for Me: Amortized Sampling for Fast and Stable Training of Energy-Based Models
Will Sussman Grathwohl, Jacob Jin Kelly, Milad Hashemi, Mohammad Norouzi, Kevin Swersky, David Duvenaud

Neural Thompson Sampling
Weitong ZHANG, Dongruo Zhou, Lihong Li, Quanquan Gu

A Design Space Study for LISTA and Beyond
Tianjian Meng, Xiaohan Chen, Yifan Jiang, Zhangyang Wang

i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning
Kibok Lee, Yian Zhu, Kihyuk Sohn, Chun-Liang Li, Jinwoo Shin, Honglak Lee

Factorizing Declarative and Procedural Knowledge in Structured, Dynamical Environments
Anirudh Goyal, Alex Lamb, Phanideep Gampa, Philippe Beaudoin, Charles Blundell, Sergey Levine, Yoshua Bengio, Michael Curtis Mozer

Calibration of Neural Networks using Splines
Kartik Gupta, Amir Rahimi, Thalaiyasingam Ajanthan, Thomas Mensink, Cristian Sminchisescu, Richard Hartley

Extreme Memorization via Scale of Initialization
Harsh Mehta, Ashok Cutkosky, Behnam Neyshabur

Molecule Optimization by Explainable Evolution
Binghong Chen, Tianzhe Wang, Chengtao Li, Hanjun Dai, Le Song

Combining Ensembles and Data Augmentation Can Harm Your Calibration
Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, Dustin Tran

Workshops
Science and Engineering of Deep Learning
Speakers and Panelists include: Alex Hanna
Moderator and Advisors include: Emily Denton
Organizers include: Negar Rostemzadeh, Samy Bengio*

Synthetic Data Generation: Quality, Privacy, Bias
Speakers include: Jinsung Yoon, Emily Denton
Program Committee includes: Syed Ashrafulla

Enormous Language Models: Perspectives and Benchmarks
Speakers and Panelists include: Noam Shazeer, Natalie Schluter
Organizers include: Colin Raffel, Adam Roberts, Jascha Sohl-Dickstein, Katherine Lee, William Fedus, Aitor Lewkowycz

The Role of Mathematical Reasoning in General Artificial Intelligence
Speakers and Panelists include: Markus Rabe, Christian Szegedy

Weakly Supervised Learning
Invited Speakers include: Lu Jiang

Learning to Learn
Organizers include: Yevgen Chebotar

Embodied Multimodal Learning (EML)
Invited Speakers includes: Sergey Levine

Distributed and Private Machine Learning
Program Committee includes: Peter Kairouz, Ananda Theertha Suresh

S2D-OLAD: From Shallow to Deep, Overcoming Limited and Adverse Data
Invited Speakers include: Alex Hanna, Hugo Larochelle
Organizers include: Vincent Dumoulin

Responsible AI (RAI)
Speakers include: Been Kim

Energy-Based Models: Current Perspectives, Challenges, and Opportunities
Organizers include: Adji Bousso Dieng, Igor Mordatch

A Roadmap to Never-Ending RL
Invited Session Panelists include: Aleksandra Faust
Program Committee includes: Coline Devin, Karol Hausman, Ben Eysenbach, Ofir Nachum, Ryan Julian, Tianhe Yu, Dumitru Erhan, Marc Pickett, Shixiang Gu

2nd Workshop on Practical ML for Developing Countries: Learning Under Limited/low Resource Scenarios
Program Committee includes: Pablo Samuel Castro

Beyond Static Papers: Rethinking How We Share Scientific Understanding in ML
Speakers include: David Ha, Hugo Larochelle
Organizers include: Sara Hooker


* Indicates work done while at Google

Source: Google AI Blog


Google at ECCV 2020

This week, the 16th European Conference on Computer Vision (ECCV2020) begins, a premier forum for the dissemination of research in computer vision and related fields. Being held virtually for the first time this year, Google is proud to be an ECCV2020 Platinum Partner and is excited to share our research with the community with nearly 50 accepted publications, alongside several tutorials and workshops.

If you are registered for ECCV this year, please visit our virtual booth in the Platinum Exhibition Hall to learn more about the research we’re presenting at ECCV 2020, including some demos and opportunities to connect with our researchers. You can also learn more about our contributions below (Google affiliations in bold).

Organizing Committee
General Chairs: Vittorio Ferrari, Bob Fisher, Cordelia Schmid, Emanuele TrucoAcademic Demonstrations Chair: Thomas Mensink

Accepted Publications
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis (Honorable Mention Award)
Ben Mildenhall, Pratul Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, Ren Ng

Quaternion Equivariant Capsule Networks for 3D Point Clouds
Yongheng Zhao, Tolga Birdal, Jan Eric Lenssen, Emanuele Menegatti, Leonidas Guibas, Federico Tombari

SoftpoolNet: Shape Descriptor for Point Cloud Completion and Classification
Yida Wang, David Joseph Tan, Nassir Navab, Federico Tombari

Combining Implicit Function Learning and Parametric Models for 3D Human Reconstruction
Bharat Lal Bhatnagar, Cristian Sminchisescu, Christian Theobalt, Gerard Pons-Moll

CoReNet: Coherent 3D scene reconstruction from a single RGB image
Stefan Popov, Pablo Bauszat, Vittorio Ferrari

Adversarial Generative Grammars for Human Activity Prediction
AJ Piergiovanni, Anelia Angelova, Alexander Toshev, Michael S. Ryoo

Self6D: Self-Supervised Monocular 6D Object Pose Estimation
Gu Wang, Fabian Manhardt, Jianzhun Shao, Xiangyang Ji, Nassir Navab, Federico Tombari

Du2Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels
Yinda Zhang, Neal Wadhwa, Sergio Orts-Escolano, Christian Häne, Sean Fanello, Rahul Garg

What Matters in Unsupervised Optical Flow
Rico Jonschkowski, Austin Stone, Jonathan T. Barron, Ariel Gordon, Kurt Konolige, Anelia Angelova

Appearance Consensus Driven Self-Supervised Human Mesh Recovery
Jogendra N. Kundu, Mugalodi Rakesh, Varun Jampani, Rahul M. Venkatesh, R. Venkatesh Babu

Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset
Menglin Jia, Mengyun Shi, Mikhail Sirotenko, Yin Cui, Claire Cardie, Bharath Hariharan, Hartwig Adam, Serge Belongie

PointMixup: Augmentation for Point Clouds
Yunlu Chen, Vincent Tao Hu, Efstratios Gavves, Thomas Mensink, Pascal Mettes1, Pengwan Yang, Cees Snoek

Connecting Vision and Language with Localized Narratives (see our blog post)
Jordi Pont-Tuset, Jasper Uijlings, Soravit Changpinyo, Radu Soricut, Vittorio Ferrari

Big Transfer (BiT): General Visual Representation Learning (see our blog post)
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, Neil Houlsby

View-Invariant Probabilistic Embedding for Human Pose
Jennifer J. Sun, Jiaping Zhao, Liang-Chieh Chen, Florian Schroff, Hartwig Adam, Ting Liu

Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation
Huiyu Wang, Yukun Zhu, Bradley Green, Hartwig Adam, Alan Yuille, Liang-Chieh Chen

Mask2CAD: 3D Shape Prediction by Learning to Segment and Retrieve
Weicheng Kuo, Anelia Angelova, Tsung-Yi Lin, Angela Dai

A Generalization of Otsu's Method and Minimum Error Thresholding
Jonathan T. Barron

Learning to Factorize and Relight a City
Andrew Liu, Shiry Ginosar, Tinghui Zhou, Alexei A. Efros, Noah Snavely

Weakly Supervised 3D Human Pose and Shape Reconstruction with Normalizing Flows
Andrei Zanfir, Eduard Gabriel Bazavan, Hongyi Xu, Bill Freeman, Rahul Sukthankar, Cristian Sminchisescu

Multi-modal Transformer for Video Retrieval
Valentin Gabeur, Chen Sun, Karteek Alahari, Cordelia Schmid

Generative Latent Textured Proxies for Category-Level Object Modeling
Ricardo Martin Brualla, Sofien Bouaziz, Matthew Brown, Rohit Pandey, Dan B Goldman

Neural Design Network: Graphic Layout Generation with Constraints
Hsin-Ying Lee*, Lu Jiang, Irfan Essa, Phuong B Le, Haifeng Gong, Ming-Hsuan Yang, Weilong Yang

Neural Articulated Shape Approximation
Boyang Deng, Gerard Pons-Moll, Timothy Jeruzalski, JP Lewis, Geoffrey Hinton, Mohammad Norouzi, Andrea Tagliasacchi

Uncertainty-Aware Weakly Supervised Action Detection from Untrimmed Videos
Anurag Arnab, Arsha Nagrani, Chen Sun, Cordelia Schmid

Beyond Controlled Environments: 3D Camera Re-Localization in Changing Indoor Scenes
Johanna Wald, Torsten Sattler, Stuart Golodetz, Tommaso Cavallari, Federico Tombari

Consistency Guided Scene Flow Estimation
Yuhua Chen, Luc Van Gool, Cordelia Schmid, Cristian Sminchisescu

Continuous Adaptation for Interactive Object Segmentation by Learning from Corrections
Theodora Kontogianni*, Michael Gygli, Jasper Uijlings, Vittorio Ferrari

SimPose: Effectively Learning DensePose and Surface Normal of People from Simulated Data
Tyler Lixuan Zhu, Per Karlsson, Christoph Bregler

Learning Data Augmentation Strategies for Object Detection
Barret Zoph, Ekin Dogus Cubuk, Golnaz Ghiasi, Tsung-Yi Lin, Jonathon Shlens, Quoc V Le

Streaming Object Detection for 3-D Point Clouds
Wei Han, Zhengdong Zhang, Benjamin Caine, Brandon Yang, Christoph Sprunk, Ouais Alsharif, Jiquan Ngiam, Vijay Vasudevan, Jonathon Shlens, Zhifeng Chen

Improving 3D Object Detection through Progressive Population Based Augmentation
Shuyang Cheng, Zhaoqi Leng, Ekin Dogus Cubuk, Barret Zoph, Chunyan Bai, Jiquan Ngiam, Yang Song, Benjamin Caine, Vijay Vasudevan, Congcong Li, Quoc V. Le, Jonathon Shlens, Dragomir Anguelov

An LSTM Approach to Temporal 3D Object Detection in LiDAR Point Clouds
Rui Huang, Wanyue Zhang, Abhijit Kundu, Caroline Pantofaru, David A Ross, Thomas Funkhouser, Alireza Fathi

BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage Models
Jiahui Yu, Pengchong Jin, Hanxiao Liu, Gabriel Bender, Pieter-Jan Kindermans, Mingxing Tan, Thomas Huang, Xiaodan Song, Ruoming Pang, Quoc Le

Memory-Efficient Incremental Learning Through Feature Adaptation
Ahmet Iscen, Jeffrey Zhang, Svetlana Lazebnik, Cordelia Schmid

Virtual Multi-view Fusion for 3D Semantic Segmentation
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David A Ross, Brian E Brewington, Thomas Funkhouser, Caroline Pantofaru

Efficient Scale-permuted Backbone with Learned Resource Distribution
Xianzhi Du, Tsung-Yi Lin, Pengchong Jin, Yin Cui, Mingxing Tan, Quoc V Le, Xiaodan Song

RetrieveGAN: Image Synthesis via Differentiable Patch Retrieval
Hung-Yu Tseng*, Hsin-Ying Lee*, Lu Jiang, Ming-Hsuan Yang, Weilong Yang

Graph convolutional networks for learning with few clean and many noisy labels
Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, Ondrej Chum, Cordelia Schmid

Deep Positional and Relational Feature Learning for Rotation-Invariant Point Cloud Analysis
Ruixuan Yu, Xin Wei, Federico Tombari, Jian Sun

Federated Visual Classification with Real-World Data Distribution
Tzu-Ming Harry Hsu, Hang Qi, Matthew Brown

Joint Bilateral Learning for Real-time Universal Photorealistic Style Transfer
Xide Xia, Meng Zhang, Tianfan Xue, Zheng Sun, Hui Fang, Brian Kulis, Jiawen Chen

AssembleNet++: Assembling Modality Representations via Attention Connections
Michael S. Ryoo, AJ Piergiovanni, Juhana Kangaspunta, Anelia Angelova

Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation
Liang-Chieh Chen, Raphael Gontijo-Lopes, Bowen Cheng, Maxwell D. Collins, Ekin D. Cubuk, Barret Zoph, Hartwig Adam, Jonathon Shlens

AttentionNAS: Spatiotemporal Attention Cell Search for Video Classification
Xiaofang Wang, Xuehan Xiong, Maxim Neumann, AJ Piergiovanni, Michael S. Ryoo, Anelia Angelova, Kris M. Kitani, Wei Hua

Unifying Deep Local and Global Features for Image Search
Bingyi Cao, Andre Araujo, Jack Sim

Pillar-based Object Detection for Autonomous Driving
Yue Wang, Alireza Fathi, Abhijit Kundu, David Ross, Caroline Pantofaru, Tom Funkhouser, Justin Solomon

Improving Object Detection with Selective Self-supervised Self-training
Yandong Li, Di Huang, Danfeng Qin, Liqiang Wang, Boqing Gong

Environment-agnostic Multitask Learning for Natural Language Grounded NavigationXin Eric Wang*, Vihan Jain, Eugene Ie, William Yang Wang, Zornitsa Kozareva, Sujith Ravi

SimAug: Learning Robust Representations from Simulation for Trajectory Prediction
Junwei Liang, Lu Jiang, Alex Hauptmann

Tutorials
New Frontiers for Learning with Limited Labels or Data
Organizers: Shalini De Mello, Sifei Liu, Zhiding Yu, Pavlo Molchanov, Varun Jampani, Arash Vahdat, Animashree Anandkumar, Jan Kautz

Weakly Supervised Learning in Computer Vision
Organizers: Seong Joon Oh, Rodrigo Benenson, Hakan Bilen

Workshops
Joint COCO and LVIS Recognition Challenge
Organizers: Alexander Kirillov, Tsung-Yi Lin, Yin Cui, Matteo Ruggero Ronchi, Agrim Gupta, Ross Girshick, Piotr Dollar

4D Vision
Organizers: Anelia Angelova, Vincent Casser, Jürgen Sturm, Noah Snavely, Rahul Sukthankar

GigaVision: When Gigapixel Videography Meets Computer Vision
Organizers: Lu Fang, Shengjin Wang, David J. Brady, Feng Yang

Advances in Image Manipulation Workshop and Challenges
Organizers: Radu Timofte, Andrey Ignatov, Luc Van Gool, Wangmeng Zuo, Ming-Hsuan Yang, Kyoung Mu Lee, Liang Lin, Eli Shechtman, Kai Zhang, Dario Fuoli, Zhiwu Huang, Martin Danelljan, Shuhang Gu, Ming-Yu Liu, Seungjun Nah, Sanghyun Son, Jaerin Lee, Andres Romero, ETH Zurich, Hannan Lu, Ruofan Zhou, Majed El Helou, Sabine Süsstrunk, Roey Mechrez, BeyondMinds & Technion, Pengxu Wei, Evangelos Ntavelis, Siavash Bigdeli

Robust Vision Challenge 2020
Organizers:Oliver Zendel, Hassan Abu Alhaija, Rodrigo Benenson, Marius Cordts, Angela Dai, Xavier Puig Fernandez, Andreas Geiger, Niklas Hanselmann, Nicolas Jourdan, Vladlen Koltun, Peter Kontschider, Alina Kuznetsova, Yubin Kang, Tsung-Yi Lin, Claudio Michaelis, Gerhard Neuhold, Matthias Niessner, Marc Pollefeys, Rene Ranftl, Carsten Rother, Torsten Sattler, Daniel Scharstein, Hendrik Schilling, Nick Schneider, Jonas Uhrig, Xiu-Shen Wei, Jonas Wulff, Bolei Zhou

“Deep Internal Learning”: Training with no prior examples
Organizers: Michal Irani,Tomer Michaeli, Tali Dekel, Assaf Shocher, Tamar Rott Shaham

Instance-Level Recognition
Organizers: Andre Araujo, Cam Askew, Bingyi Cao, Ondrej Chum, Bohyung Han, Torsten Sattler, Jack Sim, Giorgos Tolias, Tobias Weyand, Xu Zhang

Women in Computer Vision Workshop (WiCV) (Platinum Sponsor)
Panel Participation: Dina Damen, Sanja Fiddler, Zeynep Akata, Grady Booch, Rahul Sukthankar

*Work performed while at Google

Source: Google AI Blog


Google at ECCV 2020

This week, the 16th European Conference on Computer Vision (ECCV2020) begins, a premier forum for the dissemination of research in computer vision and related fields. Being held virtually for the first time this year, Google is proud to be an ECCV2020 Platinum Partner and is excited to share our research with the community with nearly 50 accepted publications, alongside several tutorials and workshops.

If you are registered for ECCV this year, please visit our virtual booth in the Platinum Exhibition Hall to learn more about the research we’re presenting at ECCV 2020, including some demos and opportunities to connect with our researchers. You can also learn more about our contributions below (Google affiliations in bold).

Organizing Committee
General Chairs: Vittorio Ferrari, Bob Fisher, Cordelia Schmid, Emanuele TrucoAcademic Demonstrations Chair: Thomas Mensink

Accepted Publications
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis (Honorable Mention Award)
Ben Mildenhall, Pratul Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, Ren Ng

Quaternion Equivariant Capsule Networks for 3D Point Clouds
Yongheng Zhao, Tolga Birdal, Jan Eric Lenssen, Emanuele Menegatti, Leonidas Guibas, Federico Tombari

SoftpoolNet: Shape Descriptor for Point Cloud Completion and Classification
Yida Wang, David Joseph Tan, Nassir Navab, Federico Tombari

Combining Implicit Function Learning and Parametric Models for 3D Human Reconstruction
Bharat Lal Bhatnagar, Cristian Sminchisescu, Christian Theobalt, Gerard Pons-Moll

CoReNet: Coherent 3D scene reconstruction from a single RGB image
Stefan Popov, Pablo Bauszat, Vittorio Ferrari

Adversarial Generative Grammars for Human Activity Prediction
AJ Piergiovanni, Anelia Angelova, Alexander Toshev, Michael S. Ryoo

Self6D: Self-Supervised Monocular 6D Object Pose Estimation
Gu Wang, Fabian Manhardt, Jianzhun Shao, Xiangyang Ji, Nassir Navab, Federico Tombari

Du2Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels
Yinda Zhang, Neal Wadhwa, Sergio Orts-Escolano, Christian Häne, Sean Fanello, Rahul Garg

What Matters in Unsupervised Optical Flow
Rico Jonschkowski, Austin Stone, Jonathan T. Barron, Ariel Gordon, Kurt Konolige, Anelia Angelova

Appearance Consensus Driven Self-Supervised Human Mesh Recovery
Jogendra N. Kundu, Mugalodi Rakesh, Varun Jampani, Rahul M. Venkatesh, R. Venkatesh Babu

Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset
Menglin Jia, Mengyun Shi, Mikhail Sirotenko, Yin Cui, Claire Cardie, Bharath Hariharan, Hartwig Adam, Serge Belongie

PointMixup: Augmentation for Point Clouds
Yunlu Chen, Vincent Tao Hu, Efstratios Gavves, Thomas Mensink, Pascal Mettes1, Pengwan Yang, Cees Snoek

Connecting Vision and Language with Localized Narratives (see our blog post)
Jordi Pont-Tuset, Jasper Uijlings, Soravit Changpinyo, Radu Soricut, Vittorio Ferrari

Big Transfer (BiT): General Visual Representation Learning (see our blog post)
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, Neil Houlsby

View-Invariant Probabilistic Embedding for Human Pose
Jennifer J. Sun, Jiaping Zhao, Liang-Chieh Chen, Florian Schroff, Hartwig Adam, Ting Liu

Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation
Huiyu Wang, Yukun Zhu, Bradley Green, Hartwig Adam, Alan Yuille, Liang-Chieh Chen

Mask2CAD: 3D Shape Prediction by Learning to Segment and Retrieve
Weicheng Kuo, Anelia Angelova, Tsung-Yi Lin, Angela Dai

A Generalization of Otsu's Method and Minimum Error Thresholding
Jonathan T. Barron

Learning to Factorize and Relight a City
Andrew Liu, Shiry Ginosar, Tinghui Zhou, Alexei A. Efros, Noah Snavely

Weakly Supervised 3D Human Pose and Shape Reconstruction with Normalizing Flows
Andrei Zanfir, Eduard Gabriel Bazavan, Hongyi Xu, Bill Freeman, Rahul Sukthankar, Cristian Sminchisescu

Multi-modal Transformer for Video Retrieval
Valentin Gabeur, Chen Sun, Karteek Alahari, Cordelia Schmid

Generative Latent Textured Proxies for Category-Level Object Modeling
Ricardo Martin Brualla, Sofien Bouaziz, Matthew Brown, Rohit Pandey, Dan B Goldman

Neural Design Network: Graphic Layout Generation with Constraints
Hsin-Ying Lee*, Lu Jiang, Irfan Essa, Phuong B Le, Haifeng Gong, Ming-Hsuan Yang, Weilong Yang

Neural Articulated Shape Approximation
Boyang Deng, Gerard Pons-Moll, Timothy Jeruzalski, JP Lewis, Geoffrey Hinton, Mohammad Norouzi, Andrea Tagliasacchi

Uncertainty-Aware Weakly Supervised Action Detection from Untrimmed Videos
Anurag Arnab, Arsha Nagrani, Chen Sun, Cordelia Schmid

Beyond Controlled Environments: 3D Camera Re-Localization in Changing Indoor Scenes
Johanna Wald, Torsten Sattler, Stuart Golodetz, Tommaso Cavallari, Federico Tombari

Consistency Guided Scene Flow Estimation
Yuhua Chen, Luc Van Gool, Cordelia Schmid, Cristian Sminchisescu

Continuous Adaptation for Interactive Object Segmentation by Learning from Corrections
Theodora Kontogianni*, Michael Gygli, Jasper Uijlings, Vittorio Ferrari

SimPose: Effectively Learning DensePose and Surface Normal of People from Simulated Data
Tyler Lixuan Zhu, Per Karlsson, Christoph Bregler

Learning Data Augmentation Strategies for Object Detection
Barret Zoph, Ekin Dogus Cubuk, Golnaz Ghiasi, Tsung-Yi Lin, Jonathon Shlens, Quoc V Le

Streaming Object Detection for 3-D Point Clouds
Wei Han, Zhengdong Zhang, Benjamin Caine, Brandon Yang, Christoph Sprunk, Ouais Alsharif, Jiquan Ngiam, Vijay Vasudevan, Jonathon Shlens, Zhifeng Chen

Improving 3D Object Detection through Progressive Population Based Augmentation
Shuyang Cheng, Zhaoqi Leng, Ekin Dogus Cubuk, Barret Zoph, Chunyan Bai, Jiquan Ngiam, Yang Song, Benjamin Caine, Vijay Vasudevan, Congcong Li, Quoc V. Le, Jonathon Shlens, Dragomir Anguelov

An LSTM Approach to Temporal 3D Object Detection in LiDAR Point Clouds
Rui Huang, Wanyue Zhang, Abhijit Kundu, Caroline Pantofaru, David A Ross, Thomas Funkhouser, Alireza Fathi

BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage Models
Jiahui Yu, Pengchong Jin, Hanxiao Liu, Gabriel Bender, Pieter-Jan Kindermans, Mingxing Tan, Thomas Huang, Xiaodan Song, Ruoming Pang, Quoc Le

Memory-Efficient Incremental Learning Through Feature Adaptation
Ahmet Iscen, Jeffrey Zhang, Svetlana Lazebnik, Cordelia Schmid

Virtual Multi-view Fusion for 3D Semantic Segmentation
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David A Ross, Brian E Brewington, Thomas Funkhouser, Caroline Pantofaru

Efficient Scale-permuted Backbone with Learned Resource Distribution
Xianzhi Du, Tsung-Yi Lin, Pengchong Jin, Yin Cui, Mingxing Tan, Quoc V Le, Xiaodan Song

RetrieveGAN: Image Synthesis via Differentiable Patch Retrieval
Hung-Yu Tseng*, Hsin-Ying Lee*, Lu Jiang, Ming-Hsuan Yang, Weilong Yang

Graph convolutional networks for learning with few clean and many noisy labels
Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, Ondrej Chum, Cordelia Schmid

Deep Positional and Relational Feature Learning for Rotation-Invariant Point Cloud Analysis
Ruixuan Yu, Xin Wei, Federico Tombari, Jian Sun

Federated Visual Classification with Real-World Data Distribution
Tzu-Ming Harry Hsu, Hang Qi, Matthew Brown

Joint Bilateral Learning for Real-time Universal Photorealistic Style Transfer
Xide Xia, Meng Zhang, Tianfan Xue, Zheng Sun, Hui Fang, Brian Kulis, Jiawen Chen

AssembleNet++: Assembling Modality Representations via Attention Connections
Michael S. Ryoo, AJ Piergiovanni, Juhana Kangaspunta, Anelia Angelova

Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation
Liang-Chieh Chen, Raphael Gontijo-Lopes, Bowen Cheng, Maxwell D. Collins, Ekin D. Cubuk, Barret Zoph, Hartwig Adam, Jonathon Shlens

AttentionNAS: Spatiotemporal Attention Cell Search for Video Classification
Xiaofang Wang, Xuehan Xiong, Maxim Neumann, AJ Piergiovanni, Michael S. Ryoo, Anelia Angelova, Kris M. Kitani, Wei Hua

Unifying Deep Local and Global Features for Image Search
Bingyi Cao, Andre Araujo, Jack Sim

Pillar-based Object Detection for Autonomous Driving
Yue Wang, Alireza Fathi, Abhijit Kundu, David Ross, Caroline Pantofaru, Tom Funkhouser, Justin Solomon

Improving Object Detection with Selective Self-supervised Self-training
Yandong Li, Di Huang, Danfeng Qin, Liqiang Wang, Boqing Gong

Environment-agnostic Multitask Learning for Natural Language Grounded NavigationXin Eric Wang*, Vihan Jain, Eugene Ie, William Yang Wang, Zornitsa Kozareva, Sujith Ravi

SimAug: Learning Robust Representations from Simulation for Trajectory Prediction
Junwei Liang, Lu Jiang, Alex Hauptmann

Tutorials
New Frontiers for Learning with Limited Labels or Data
Organizers: Shalini De Mello, Sifei Liu, Zhiding Yu, Pavlo Molchanov, Varun Jampani, Arash Vahdat, Animashree Anandkumar, Jan Kautz

Weakly Supervised Learning in Computer Vision
Organizers: Seong Joon Oh, Rodrigo Benenson, Hakan Bilen

Workshops
Joint COCO and LVIS Recognition Challenge
Organizers: Alexander Kirillov, Tsung-Yi Lin, Yin Cui, Matteo Ruggero Ronchi, Agrim Gupta, Ross Girshick, Piotr Dollar

4D Vision
Organizers: Anelia Angelova, Vincent Casser, Jürgen Sturm, Noah Snavely, Rahul Sukthankar

GigaVision: When Gigapixel Videography Meets Computer Vision
Organizers: Lu Fang, Shengjin Wang, David J. Brady, Feng Yang

Advances in Image Manipulation Workshop and Challenges
Organizers: Radu Timofte, Andrey Ignatov, Luc Van Gool, Wangmeng Zuo, Ming-Hsuan Yang, Kyoung Mu Lee, Liang Lin, Eli Shechtman, Kai Zhang, Dario Fuoli, Zhiwu Huang, Martin Danelljan, Shuhang Gu, Ming-Yu Liu, Seungjun Nah, Sanghyun Son, Jaerin Lee, Andres Romero, ETH Zurich, Hannan Lu, Ruofan Zhou, Majed El Helou, Sabine Süsstrunk, Roey Mechrez, BeyondMinds & Technion, Pengxu Wei, Evangelos Ntavelis, Siavash Bigdeli

Robust Vision Challenge 2020
Organizers:Oliver Zendel, Hassan Abu Alhaija, Rodrigo Benenson, Marius Cordts, Angela Dai, Xavier Puig Fernandez, Andreas Geiger, Niklas Hanselmann, Nicolas Jourdan, Vladlen Koltun, Peter Kontschider, Alina Kuznetsova, Yubin Kang, Tsung-Yi Lin, Claudio Michaelis, Gerhard Neuhold, Matthias Niessner, Marc Pollefeys, Rene Ranftl, Carsten Rother, Torsten Sattler, Daniel Scharstein, Hendrik Schilling, Nick Schneider, Jonas Uhrig, Xiu-Shen Wei, Jonas Wulff, Bolei Zhou

“Deep Internal Learning”: Training with no prior examples
Organizers: Michal Irani,Tomer Michaeli, Tali Dekel, Assaf Shocher, Tamar Rott Shaham

Instance-Level Recognition
Organizers: Andre Araujo, Cam Askew, Bingyi Cao, Ondrej Chum, Bohyung Han, Torsten Sattler, Jack Sim, Giorgos Tolias, Tobias Weyand, Xu Zhang

Women in Computer Vision Workshop (WiCV) (Platinum Sponsor)
Panel Participation: Dina Damen, Sanja Fiddler, Zeynep Akata, Grady Booch, Rahul Sukthankar

*Work performed while at Google

Source: Google AI Blog


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).

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

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

Publications
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

Workshops
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

Committee
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).

Committees
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

Demos
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

Workshops
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

Tutorials
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

Posters
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

Workshops

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

Tutorials

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

Publications
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

Workshops
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 ICCV 2019



This week, Seoul, South Korea hosts the International Conference on Computer Vision 2019 (ICCV 2019), one of the world's premier conferences on computer vision. As a leader in computer vision research and a Gold Sponsor, Google will have a strong presence at ICCV 2019 with over 200 Googlers in attendance, more than 40 research presentations, and involvement in the organization of a number of workshops and tutorials.

If you are attending ICCV this year, please stop by our booth. There you can chat with researchers who are actively pursuing the latest innovations in computer vision and demo some of their latest research, including the technology behind MediaPipe, the new Open Images dataset, new developments for Google Lens and much more.

This year Google researchers are recipients of three prestigious ICCV awards:
More details about the Google research being presented at ICCV 2019 can be found below (Google affiliations in blue).

Organizing Committee includes:
Ming-Hsuan Yang (Program Chair)

Oral Presentations
Learning Single Camera Depth Estimation using Dual-Pixels
Rahul Garg, Neal Wadhwa, Sameer Ansari, Jonathan Barron 

RIO: 3D Object Instance Re-Localization in Changing Indoor Environments
Johanna Wald, Armen Avetisyan, Nassir Navab, Federico Tombari, Matthias Niessner 

ShapeMask: Learning to Segment Novel Objects by Refining Shape Priors
Weicheng Kuo, Anelia Angelova, Jitendra Malik, Tsung-Yi Lin 

PuppetGAN: Cross-Domain Image Manipulation by Demonstration
Ben Usman, Nick Dufour, Kate Saenko, Chris Bregler

COCO-GAN: Generation by Parts via Conditional Coordinating
Chieh Hubert Lin, Chia-Che Chang, Yu-Sheng Chen, Da-Cheng Juan, Wei Wei, Hwann-Tzong Chen

Towards Unconstrained End-to-End Text Spotting
Siyang Qin, Alessandro Bissaco, Michalis Raptis, Yasuhisa Fujii, Ying Xiao

SinGAN: Learning a Generative Model from a Single Natural Image
Tamar Rott Shaham, Tali Dekel, Tomer Michaeli 
(ICCV 2019 Marr Prize Winner — Best Paper Award)

Generative Modeling for Small-Data Object Detection
Lanlan Liu, Michael Muelly, Jia Deng, Tomas Pfister, Li-Jia Li 

Searching for MobileNetV3
Andrew Howard, Mark Sandler, Bo Chen, Weijun Wang, Liang-Chieh Chen, Mingxing Tan, Grace Chu, Vijay Vasudevan, Yukun Zhu, Ruoming Pang, Hartwig Adam, Quoc Le 

S⁴L: Self-Supervised Semi-supervised Learning
Lucas Beyer, Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov 

Sampling-Free Epistemic Uncertainty Estimation Using Approximated Variance Propagation
Janis Postels, Francesco Ferroni, Huseyin Coskun, Nassir Navab, Federico Tombari

Linearized Multi-sampling for Differentiable Image Transformation
Wei Jiang, Weiwei Sun, Andrea Tagliasacchi, Eduard Trulls, Kwang Moo Yi 

Poster Presentations
ELF: Embedded Localisation of Features in Pre-trained CNN
Assia Benbihi, Matthieu Geist, Cedric Pradalier 

Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras
Ariel Gordon, Hanhan Li, Rico Jonschkowski, Anelia Angelova

ForkNet: Multi-branch Volumetric Semantic Completion from a Single Depth Image
Yida Wang, David Joseph Tan, Nassir Navab, Federico Tombari 

A Learned Representation for Scalable Vector Graphics
Raphael Gontijo Lopes, David Ha, Douglas Eck, Jonathon Shlens 

FrameNet: Learning Local Canonical Frames of 3D Surfaces from a Single RGB Image
Jingwei Huang, Yichao Zhou, Thomas Funkhouser, Leonidas Guibas

Prior-Aware Neural Network for Partially-Supervised Multi-Organ Segmentation
Yuyin Zhou, Zhe Li, Song Bai, Xinlei Chen, Mei Han, Chong Wang, Elliot Fishman, Alan Yuille 

Boundless: Generative Adversarial Networks for Image Extension
Dilip Krishnan, Piotr Teterwak, Aaron Sarna, Aaron Maschinot, Ce Liu, David Belanger, William Freeman

Cap2Det: Learning to Amplify Weak Caption Supervision for Object Detection
Keren Ye, Mingda Zhang, Adriana Kovashka, Wei Li, Danfeng Qin, Jesse Berent 

NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-supervised Object Detection
Jiyang Gao, Jiang Wang, Shengyang Dai, Li-Jia Li, Ram Nevatia 

Object-Driven Multi-Layer Scene Decomposition from a Single Image
Helisa Dhamo, Nassir Navab, Federico Tombari 

Improving Adversarial Robustness via Guided Complement Entropy
Hao-Yun Chen, Jhao-Hong Liang, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, Da-Cheng Juan 

XRAI: Better Attributions Through Regions
Andrei Kapishnikov, Tolga Bolukbasi, Fernanda Viegas, Michael Terry

SegSort: Segment Sorting for Semantic Segmentation
Jyh-Jing Hwang, Stella Yu, Jianbo Shi, Maxwell Collins, Tien-Ju Yang, Xiao Zhang, Liang-Chieh Chen 

Self-Supervised Learning with Geometric Constraints in Monocular Video: Connecting Flow, Depth, and Camera
Yuhua Chen, Cordelia Schmid, Cristian Sminchisescu 

VideoBERT: A Joint Model for Video and Language Representation Learning
Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, Cordelia Schmid 

Explaining the Ambiguity of Object Detection and 6D Pose from Visual Data
Fabian Manhardt, Diego Martín Arroyo, Christian Rupprecht, Benjamin  Busam, Tolga Birdal, Nassir Navab, Federico Tombari 

Constructing Self-Motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation
Qing Lian, Lixin Duan, Fengmao Lv, Boqing Gong 

Learning Shape Templates Using Structured Implicit Functions
Kyle Genova, Forrester Cole, Daniel Vlasic, Aaron Sarna, William Freeman, Thomas Funkhouser

Transferable Representation Learning in Vision-and-Language Navigation
Haoshuo Huang, Vihan Jain, Harsh Mehta, Alexander Ku, Gabriel Magalhaes, Jason Baldridge, Eugene Ie 

Controllable Attention for Structured Layered Video Decomposition
Jean-Baptiste Alayrac, Joao Carreira, Relja Arandjelović, Andrew Zisserman

Pixel2Mesh++: Multi-view 3D Mesh Generation via Deformation
Chao Wen, Yinda Zhang, Zhuwen Li, Yanwei Fu

Beyond Cartesian Representations for Local Descriptors
Patrick Ebel, Anastasiia Mishchuk, Kwang Moo Yi, Pascal Fua, Eduard Trulls

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data
Xiangyu Yue, Yang Zhang, Sicheng Zhao, Alberto Sangiovanni-Vincentelli, Kurt Keutzer, Boqing Gong 

Evolving Space-Time Neural Architectures for Videos
AJ Piergiovanni, Anelia Angelova, Alexander Toshev, Michael Ryoo 

Moulding Humans: Non-parametric 3D Human Shape Estimation from Single Images
Valentin Gabeur, Jean-Sebastien Franco, Xavier Martin, Cordelia Schmid, Gregory Rogez

Multi-view Image Fusion
Marc Comino Trinidad, Ricardo Martin-Brualla, Florian Kainz, Janne Kontkanen 

EvalNorm: Estimating Batch Normalization Statistics for Evaluation
Saurabh Singh, Abhinav Shrivastava

Attention Augmented Convolutional Networks
Irwan Bello, Barret Zoph, Quoc Le, Ashish Vaswani, Jonathon Shlens 

Patchwork: A Patch-wise Attention Network for Efficient Object Detection and Segmentation in Video Streams
Yuning Chai

Workshops
Low-Power Computer Vision
Organizers include: Bo Chen

Neural Architects
Organizers include: Barret Zoph

The 3rd YouTube-8M Large-Scale Video Understanding Workshop
Organizers include: Paul NatsevCordelia SchmidRahul SukthankarJoonseok LeeGeorge Toderici

Should We Pre-register Experiments in Computer Vision?
Organizers include: Jack Valmadre

Extreme Vision Modeling
Organizers include: Rahul Sukthankar

Joint COCO and Mapillary Recognition Challenge
Organizers include: Tsung-Yi Lin, Yin Cui

Open Images Challenge
Organizers include: Vittorio Ferrari, Alina Kuznetsova, Rodrigo Benenson, Victor Gomes, Matteo Malloci

Tutorials
Meta-Learning and Metric Learning Algorithms
Organizers include: Kevin Swersky

Source: Google AI Blog


Google at ACL 2019



This week, Florence, Italy hosts the 2019 Annual Meeting of the Association for Computational Linguistics (ACL 2019), the premier conference in the field of natural language understanding, covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

As a leader in natural language processing and understanding, and a Diamond Level sponsor of ACL 2019, Google will be on hand to showcase the latest research on syntax, semantics, discourse, conversation, multilingual modeling, sentiment analysis, question answering, summarization, and generally building better systems using labeled and unlabeled data.

If you’re attending ACL 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 demo the Natural Questions corpus, the Multilingual Universal Sentence Encoder and more. You can also learn more about the Google research being presented at ACL 2019 below (Google affiliations in blue).

Organizing Committee includes:
Enrique Alfonseca

Accepted Publications
A Joint Named-Entity Recognizer for Heterogeneous Tag-sets Using a Tag Hierarchy
Genady Beryozkin, Yoel Drori, Oren Gilon, Tzvika Hartman, Idan Szpektor

Do Neural Dialog Systems Use the Conversation History Effectively? An Empirical Study
Chinnadhurai Sankar, Sandeep Subramanian, Chris Pal, Sarath Chandar, Yoshua Bengio

Generating Logical Forms from Graph Representations of Text and Entities
Peter Shaw, Philip Massey, Angelica Chen, Francesco Piccinno, Yasemin Altun

Extracting Symptoms and their Status from Clinical Conversations
Nan Du, Kai Chen, Anjuli Kannan, Linh Trans, Yuhui Chen, Izhak Shafran

Stay on the Path: Instruction Fidelity in Vision-and-Language Navigation
Vihan Jain, Gabriel Magalhaes, Alexander Ku, Ashish Vaswani, Eugene Le, Jason Baldridge

Meaning to Form: Measuring Systematicity as Information
Tiago Pimentel, Arya D. McCarthy, Damian Blasi, Brian Roark, Ryan Cotterell

Matching the Blanks: Distributional Similarityfor Relation Learning
Livio Baldini Soares, Nicholas FitzGerald, Jeffrey Ling, Tom Kwiatkowski

Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc Le, Ruslan Salakhutdinov

HighRES: Highlight-based Reference-less Evaluation of Summarization
Hardy Hardy, Shashi Narayan, Andreas Vlachos

Zero-Shot Entity Linking by Reading Entity Descriptions
Lajanugen Logeswaran, Ming-Wei Chang, Kristina Toutanova, Kenton Lee, Jacob Devlin, Honglak Lee

Robust Neural Machine Translation with Doubly Adversarial Inputs
Yong Cheng, Lu Jiang, Wolfgang Macherey

Natural Questions: a Benchmark for Question Answering Research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Matthew Kelcey, Jacob Devlin, Kenton Lee, Kristina N. Toutanova, Llion Jones, Ming-Wei Chang, Andrew Dai, Jakob Uszkoreit, Quoc Le, Slav Petrov

Like a Baby: Visually Situated Neural Language Acquisition
Alexander Ororbia, Ankur Mali, Matthew Kelly, David Reitter

What Kind of Language Is Hard to Language-Model?
Sebastian J. Mielke, Ryan Cotterell, Kyle Gorman, Brian Roark, Jason Eisner

How Multilingual is Multilingual BERT?
Telmo Pires, Eva Schlinger, Dan Garrette

Handling Divergent Reference Texts when Evaluating Table-to-Text Generation
Bhuwan Dhingra, Manaal Faruqui, Ankur Parikh, Ming-Wei Chang, Dipanjan Das, William Cohen

BAM! Born-Again Multi-Task Networks for Natural Language Understanding
Kevin Clark, Minh-Thang Luong, Urvashi Khandelal, Christopher D. Manning, Quoc V. Le

Dynamically Composing Domain-Data Selection with Clean-Data Selection by “Co-Curricular Learning" for Neural Machine Translation
Wei Wang, Isaac Caswell, Ciprian Chelba

Monotonic Infinite Lookback Attention for Simultaneous Machine Translation
Naveen Arivazhagan, Colin Cherry, Wolfgang Macherey, Chung-Cheng Chiu, Semih Yavuz, Ruoming Pang, Wei Li, Colin Raffel

On the Robustness of Self-Attentive Models
Yu-Lun Hsieh, Minhao Cheng, Da-Cheng Juan, Wei Wei, Wen-Lian Hsu, Cho-Jui Hsieh

Neural Decipherment via Minimum-Cost Flow: from Ugaritic to Linear B
Jiaming Luo, Yuan Cao, Regina Barzilay

How Large Are Lions? Inducing Distributions over Quantitative Attributes
Yanai Elazar, Abhijit Mahabal, Deepak Ramachandran, Tania Bedrax-Weiss, Dan Roth

BERT Rediscovers the Classical NLP Pipeline
Ian Tenney, Dipanjan Das, Ellie Pavlick

Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling
Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R. Thomas Mccoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, Berlin Chen, Benjamin Van Durme, Edouard Grave, Ellie Pavlick, Samuel R. Bowman

Robust Zero-Shot Cross-Domain Slot Filling with Example Values
Darsh Shah, Raghav Gupta, Amir Fayazi, Dilek Hakkani-Tur

Latent Retrieval for Weakly Supervised Open Domain Question Answering
Kenton Lee, Ming-Wei Chang, Kristina Toutanova

On-device Structured and Context Partitioned Projection Networks
Sujith Ravi, Zornitsa Kozareva

Incorporating Priors with Feature Attribution on Text Classification
Frederick Liu, Besim Avci

Informative Image Captioning with External Sources of Information
Sanqiang Zhao, Piyush Sharma, Tomer Levinboim, Radu Soricut

Reducing Word Omission Errors in Neural Machine Translation: A Contrastive Learning Approach
Zonghan Yang, Yong Cheng, Yang Liu, Maosong Sun

Synthetic QA Corpora Generation with Roundtrip Consistency
Chris Alberti, Daniel Andor, Emily Pitler, Jacob Devlin, Michael Collins

Unsupervised Paraphrasing without Translation
Aurko Roy, David Grangier

Workshops
Widening NLP 2019
Organizers include: Diyi Yang

NLP for Conversational AI
Organizers include: Thang-Minh Luong, Tania Bedrax-Weiss

The Fourth Arabic Natural Language Processing Workshop
Organizers include: Imed Zitouni

The Third Workshop on Abusive Language Online
Organizers include: Zeerak Waseem

TyP-NLP, Typology for Polyglot NLP
Organizers include: Manaal Faruqui

Gender Bias in Natural Language Processing
Organizers include: Kellie Webster

Tutorials
Wikipedia as a Resource for Text Analysis and Retrieval
Organizer: Marius Pasca

Source: Google AI Blog