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Google at ICLR 2023

Posted by Catherine Armato, Program Manager, Google

The Eleventh International Conference on Learning Representations (ICLR 2023) is being held this week as a hybrid event in Kigali, Rwanda. We are proud to be a Diamond Sponsor of ICLR 2023, a premier conference on deep learning, where Google researchers contribute at all levels. This year we are presenting over 100 papers and are actively involved in organizing and hosting a number of different events, including workshops and interactive sessions.

If you’re registered for ICLR 2023, we hope you’ll visit the Google booth to learn more about the exciting work we’re doing across topics spanning representation and reinforcement learning, theory and optimization, social impact, safety and privacy, and applications from generative AI to speech and robotics. Continue below to find the many ways in which Google researchers are engaged at ICLR 2023, including workshops, papers, posters and talks (Google affiliations in bold).

Board and Organizing Committee

Board Members include: Shakir Mohamed, Tara Sainath

Senior Program Chairs include: Been Kim

Workshop Chairs include: Aisha Walcott-Bryant, Rose Yu

Diversity, Equity & Inclusion Chairs include: Rosanne Liu

Outstanding Paper awards

Emergence of Maps in the Memories of Blind Navigation Agents
Erik Wijmans, Manolis Savva, Irfan Essa, Stefan Lee, Ari S. Morcos, Dhruv Batra

DreamFusion: Text-to-3D Using 2D Diffusion
Ben Poole, Ajay Jain, Jonathan T. Barron, Ben Mildenhall

Keynote speaker

Learned Optimizers: Why They're the Future, Why They’re Hard, and What They Can Do Now
Jascha Sohl-Dickstein

Workshops

[email protected] 2023: ML Solutions in Africa
Organizers include: Julia Elliott, Phil Culliton, Ray Harvey
Facilitators: Julia Elliot, Walter Reade

Reincarnating Reinforcement Learning (Reincarnating RL)
Organizers include: Rishabh Agarwal, Ted Xiao, Max Schwarzer
Speakers include: Sergey Levine
Panelists include: Marc G. Bellemare, Sergey Levine

Trustworthy and Reliable Large-Scale Machine Learning Models
Organizers include: Sanmi Koyejo
Speakers include: Nicholas Carlini

Physics for Machine Learning (Physics4ML)
Speakers include: Yasaman Bahri

AI for Agent-Based Modelling Community (AI4ABM)
Organizers include: Pablo Samuel Castro

Mathematical and Empirical Understanding of Foundation Models (ME-FoMo)
Organizers include: Mathilde Caron, Tengyu Ma, Hanie Sedghi
Speakers include: Yasaman Bahri, Yann Dauphin

Neurosymbolic Generative Models 2023 (NeSy-GeMs)
Organizers include: Kevin Ellis
Speakers include: Daniel Tarlow, Tuan Anh Le

What Do We Need for Successful Domain Generalization?
Panelists include: Boqing Gong

The 4th Workshop on Practical ML for Developing Countries: Learning Under Limited/Low Resource Settings
Keynote Speaker: Adji Bousso Dieng

Machine Learning for Remote Sensing
Speakers include: Abigail Annkah

Multimodal Representation Learning (MRL): Perks and Pitfalls
Organizers include: Petra Poklukar
Speakers include: Arsha Nagrani

Pitfalls of Limited Data and Computation for Trustworthy ML
Organizers include: Prateek Jain
Speakers include: Nicholas Carlini, Praneeth Netrapalli

Sparsity in Neural Networks: On Practical Limitations and Tradeoffs Between Sustainability and Efficiency
Organizers include: Trevor Gale, Utku Evci
Speakers include: Aakanksha Chowdhery, Jeff Dean

Time Series Representation Learning for Health
Speakers include: Katherine Heller

Deep Learning for Code (DL4C)
Organizers include: Gabriel Orlanski
Speakers include: Alex Polozov, Daniel Tarlow

Affinity Workshops

Tiny Papers Showcase Day (a DEI initiative)
Organizers include: Rosanne Liu

Papers

Evolve Smoothly, Fit Consistently: Learning Smooth Latent Dynamics for Advection-Dominated Systems
Zhong Yi Wan, Leonardo Zepeda-Nunez, Anudhyan Boral, Fei Sha

Quantifying Memorization Across Neural Language Models
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramer, Chiyuan Zhang

Emergence of Maps in the Memories of Blind Navigation Agents (Outstanding Paper Award)
Erik Wijmans, Manolis Savva, Irfan Essa, Stefan Lee, Ari S. Morcos, Dhruv Batra

Offline Q-Learning on Diverse Multi-task Data Both Scales and Generalizes (see blog post)
Aviral Kumar, Rishabh Agarwal, Xingyang Geng, George Tucker, Sergey Levine

ReAct: Synergizing Reasoning and Acting in Language Models (see blog post)
Shunyu Yao*, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R. Narasimhan, Yuan Cao

Prompt-to-Prompt Image Editing with Cross-Attention Control
Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, Daniel Cohen-Or

DreamFusion: Text-to-3D Using 2D Diffusion (Outstanding Paper Award)
Ben Poole, Ajay Jain, Jonathan T. Barron, Ben Mildenhall

A System for Morphology-Task Generalization via Unified Representation and Behavior Distillation
Hiroki Furuta, Yusuke Iwasawa, Yutaka Matsuo, Shixiang Shane Gu

Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier
Pierluca D'Oro, Max Schwarzer, Evgenii Nikishin, Pierre-Luc Bacon, Marc G Bellemare, Aaron Courville

Dichotomy of Control: Separating What You Can Control from What You Cannot
Sherry Yang, Dale Schuurmans, Pieter Abbeel, Ofir Nachum

Fast and Precise: Adjusting Planning Horizon with Adaptive Subgoal Search
Michał Zawalski, Michał Tyrolski, Konrad Czechowski, Tomasz Odrzygóźdź, Damian Stachura, Piotr Piekos, Yuhuai Wu, Łukasz Kucinski, Piotr Miłos

The Trade-Off Between Universality and Label Efficiency of Representations from Contrastive Learning
Zhenmei Shi, Jiefeng Chen, Kunyang Li, Jayaram Raghuram, Xi Wu, Yingyu Liang, Somesh Jha

Sparsity-Constrained Optimal Transport
Tianlin Liu*, Joan Puigcerver, Mathieu Blondel

Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask?
Mansheej Paul, Feng Chen, Brett W. Larsen, Jonathan Frankle, Surya Ganguli, Gintare Karolina Dziugaite

Extreme Q-Learning: MaxEnt RL without Entropy
Divyansh Garg, Joey Hejna, Matthieu Geist, Stefano Ermon

Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs
Albert Qiaochu Jiang, Sean Welleck, Jin Peng Zhou, Timothee Lacroix, Jiacheng Liu, Wenda Li, Mateja Jamnik, Guillaume Lample, Yuhuai Wu

SimPer: Simple Self-Supervised Learning of Periodic Targets
Yuzhe Yang, Xin Liu, Jiang Wu, Silviu Borac, Dina Katabi, Ming-Zher Poh, Daniel McDuff

Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language
Andy Zeng, Maria Attarian, Brian Ichter, Krzysztof Marcin Choromanski, Adrian Wong, Stefan Welker, Federico Tombari, Aveek Purohit, Michael S. Ryoo, Vikas Sindhwani, Johnny Lee, Vincent Vanhoucke, Pete Florence

What Learning Algorithm Is In-Context Learning? Investigations with Linear Models
Ekin Akyurek*, Dale Schuurmans, Jacob Andreas, Tengyu Ma*, Denny Zhou

Preference Transformer: Modeling Human Preferences Using Transformers for RL
Changyeon Kim, Jongjin Park, Jinwoo Shin, Honglak Lee, Pieter Abbeel, Kimin Lee

Iterative Patch Selection for High-Resolution Image Recognition
Benjamin Bergner, Christoph Lippert, Aravindh Mahendran

Open-Vocabulary Object Detection upon Frozen Vision and Language Models
Weicheng Kuo, Yin Cui, Xiuye Gu, AJ Piergiovanni, Anelia Angelova

(Certified!!) Adversarial Robustness for Free!
Nicholas Carlini, Florian Tramér, Krishnamurthy (Dj) Dvijotham, Leslie Rice, Mingjie Sun, J. Zico Kolter

REPAIR: REnormalizing Permuted Activations for Interpolation Repair
Keller Jordan, Hanie Sedghi, Olga Saukh, Rahim Entezari, Behnam Neyshabur

Discrete Predictor-Corrector Diffusion Models for Image Synthesis
José Lezama, Tim Salimans, Lu Jiang, Huiwen Chang, Jonathan Ho, Irfan Essa

Feature Reconstruction From Outputs Can Mitigate Simplicity Bias in Neural Networks
Sravanti Addepalli, Anshul Nasery, Praneeth Netrapalli, Venkatesh Babu R., Prateek Jain

An Exact Poly-time Membership-Queries Algorithm for Extracting a Three-Layer ReLU Network
Amit Daniely, Elad Granot

Language Models Are Multilingual Chain-of-Thought Reasoners
Freda Shi, Mirac Suzgun, Markus Freitag, Xuezhi Wang, Suraj Srivats, Soroush Vosoughi, Hyung Won Chung, Yi Tay, Sebastian Ruder, Denny Zhou, Dipanjan Das, Jason Wei

Scaling Forward Gradient with Local Losses
Mengye Ren*, Simon Kornblith, Renjie Liao, Geoffrey Hinton

Treeformer: Dense Gradient Trees for Efficient Attention Computation
Lovish Madaan, Srinadh Bhojanapalli, Himanshu Jain, Prateek Jain

LilNetX: Lightweight Networks with EXtreme Model Compression and Structured Sparsification
Sharath Girish, Kamal Gupta, Saurabh Singh, Abhinav Shrivastava

DiffusER: Diffusion via Edit-Based Reconstruction
Machel Reid, Vincent J. Hellendoorn, Graham Neubig

Leveraging Unlabeled Data to Track Memorization
Mahsa Forouzesh, Hanie Sedghi, Patrick Thiran

A Mixture-of-Expert Approach to RL-Based Dialogue Management
Yinlam Chow, Aza Tulepbergenov, Ofir Nachum, Dhawal Gupta, Moonkyung Ryu, Mohammad Ghavamzadeh, Craig Boutilier

Easy Differentially Private Linear Regression
Kareem Amin, Matthew Joseph, Monica Ribero, Sergei Vassilvitskii

KwikBucks: Correlation Clustering with Cheap-Weak and Expensive-Strong Signals
Sandeep Silwal*, Sara Ahmadian, Andrew Nystrom, Andrew McCallum, Deepak Ramachandran, Mehran Kazemi

Massively Scaling Heteroscedastic Classifiers
Mark Collier, Rodolphe Jenatton, Basil Mustafa, Neil Houlsby, Jesse Berent, Effrosyni Kokiopoulou

The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers
Zonglin Li, Chong You, Srinadh Bhojanapalli, Daliang Li, Ankit Singh Rawat, Sashank J. Reddi, Ke Ye, Felix Chern, Felix Yu, Ruiqi Guo, Sanjiv Kumar

Compositional Semantic Parsing with Large Language Models
Andrew Drozdov, Nathanael Scharli, Ekin Akyurek, Nathan Scales, Xinying Song, Xinyun Chen, Olivier Bousquet, Denny Zhou

Extremely Simple Activation Shaping for Out-of-Distribution Detection
Andrija Djurisic, Nebojsa Bozanic, Arjun Ashok, Rosanne Liu

Long Range Language Modeling via Gated State Spaces
Harsh Mehta, Ankit Gupta, Ashok Cutkosky, Behnam Neyshabur

Investigating Multi-task Pretraining and Generalization in Reinforcement Learning
Adrien Ali Taiga, Rishabh Agarwal, Jesse Farebrother, Aaron Courville, Marc G. Bellemare

Learning Low Dimensional State Spaces with Overparameterized Recurrent Neural Nets
Edo Cohen-Karlik, Itamar Menuhin-Gruman, Raja Giryes, Nadav Cohen, Amir Globerson

Weighted Ensemble Self-Supervised Learning
Yangjun Ruan*, Saurabh Singh, Warren Morningstar, Alexander A. Alemi, Sergey Ioffe, Ian Fischer, Joshua V. Dillon

Calibrating Sequence Likelihood Improves Conditional Language Generation
Yao Zhao, Misha Khalman, Rishabh Joshi, Shashi Narayan, Mohammad Saleh, Peter J. Liu

SMART: Sentences as Basic Units for Text Evaluation
Reinald Kim Amplayo, Peter J. Liu, Yao Zhao, Shashi Narayan

Leveraging Importance Weights in Subset Selection
Gui Citovsky, Giulia DeSalvo, Sanjiv Kumar, Srikumar Ramalingam, Afshin Rostamizadeh, Yunjuan Wang*

Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks
Jesse Farebrother, Joshua Greaves, Rishabh Agarwal, Charline Le Lan, Ross Goroshin, Pablo Samuel Castro, Marc G. Bellemare

An Extensible Multi-modal Multi-task Object Dataset with Materials
Trevor Standley, Ruohan Gao, Dawn Chen, Jiajun Wu, Silvio Savarese

Measuring Forgetting of Memorized Training Examples
Matthew Jagielski, Om Thakkar, Florian Tramér, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Chiyuan Zhang

Bidirectional Language Models Are Also Few-Shot Learners
Ajay Patel, Bryan Li, Mohammad Sadegh Rasooli, Noah Constant, Colin Raffel, Chris Callison-Burch

Is Attention All That NeRF Needs?
Mukund Varma T., Peihao Wang, Xuxi Chen, Tianlong Chen, Subhashini Venugopalan, Zhangyang Wang

Automating Nearest Neighbor Search Configuration with Constrained Optimization
Philip Sun, Ruiqi Guo, Sanjiv Kumar

Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions
David Bieber, Rishab Goel, Daniel Zheng, Hugo Larochelle, Daniel Tarlow

Composing Ensembles of Pre-trained Models via Iterative Consensus
Shuang Li, Yilun Du, Joshua B. Tenenbaum, Antonio Torralba, Igor Mordatch

Λ-DARTS: Mitigating Performance Collapse by Harmonizing Operation Selection Among Cells
Sajad Movahedi, Melika Adabinejad, Ayyoob Imani, Arezou Keshavarz, Mostafa Dehghani, Azadeh Shakery, Babak N. Araabi

Blurring Diffusion Models
Emiel Hoogeboom, Tim Salimans

Part-Based Models Improve Adversarial Robustness
Chawin Sitawarin, Kornrapat Pongmala, Yizheng Chen, Nicholas Carlini, David Wagner

Learning in Temporally Structured Environments
Matt Jones, Tyler R. Scott, Mengye Ren, Gamaleldin ElSayed, Katherine Hermann, David Mayo, Michael C. Mozer

SlotFormer: Unsupervised Visual Dynamics Simulation with Object-Centric Models
Ziyi Wu, Nikita Dvornik, Klaus Greff, Thomas Kipf, Animesh Garg

Robust Algorithms on Adaptive Inputs from Bounded Adversaries
Yeshwanth Cherapanamjeri, Sandeep Silwal, David P. Woodruff, Fred Zhang, Qiuyi (Richard) Zhang, Samson Zhou

Agnostic Learning of General ReLU Activation Using Gradient Descent
Pranjal Awasthi, Alex Tang, Aravindan Vijayaraghavan

Analog Bits: Generating Discrete Data Using Diffusion Models with Self-Conditioning
Ting Chen, Ruixiang Zhang, Geoffrey Hinton

Any-Scale Balanced Samplers for Discrete Space
Haoran Sun*, Bo Dai, Charles Sutton, Dale Schuurmans, Hanjun Dai

Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation
Ziqi Wang*, Yuexin Wu, Frederick Liu, Daogao Liu, Le Hou, Hongkun Yu, Jing Li, Heng Ji

Beyond Lipschitz: Sharp Generalization and Excess Risk Bounds for Full-Batch GD
Konstantinos E. Nikolakakis, Farzin Haddadpour, Amin Karbasi, Dionysios S. Kalogerias

Causal Estimation for Text Data with (Apparent) Overlap Violations
Lin Gui, Victor Veitch

Contrastive Learning Can Find an Optimal Basis for Approximately View-Invariant Functions
Daniel D. Johnson, Ayoub El Hanchi, Chris J. Maddison

Differentially Private Adaptive Optimization with Delayed Preconditioners
Tian Li, Manzil Zaheer, Ziyu Liu, Sashank Reddi, Brendan McMahan, Virginia Smith

Distributionally Robust Post-hoc Classifiers Under Prior Shifts
Jiaheng Wei*, Harikrishna Narasimhan, Ehsan Amid, Wen-Sheng Chu, Yang Liu, Abhishek Kumar

Human Alignment of Neural Network Representations
Lukas Muttenthaler, Jonas Dippel, Lorenz Linhardt, Robert A. Vandermeulen, Simon Kornblith

Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data
Spencer Frei, Gal Vardi, Peter Bartlett, Nathan Srebro, Wei Hu

Koopman Neural Operator Forecaster for Time-Series with Temporal Distributional Shifts
Rui Wang*, Yihe Dong, Sercan Ö. Arik, Rose Yu

Latent Variable Representation for Reinforcement Learning
Tongzheng Ren, Chenjun Xiao, Tianjun Zhang, Na Li, Zhaoran Wang, Sujay Sanghavi, Dale Schuurmans, Bo Dai

Least-to-Most Prompting Enables Complex Reasoning in Large Language Models
Denny Zhou, Nathanael Scharli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc Le, Ed Chi

Mind's Eye: Grounded Language Model Reasoning Through Simulation
Ruibo Liu, Jason Wei, Shixiang Shane Gu, Te-Yen Wu, Soroush Vosoughi, Claire Cui, Denny Zhou, Andrew M. Dai

MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models
Chenglin Yang*, Siyuan Qiao, Qihang Yu, Xiaoding Yuan, Yukun Zhu, Alan Yuille, Hartwig Adam, Liang-Chieh Chen

Novel View Synthesis with Diffusion Models
Daniel Watson, William Chan, Ricardo Martin-Brualla, Jonathan Ho, Andrea Tagliasacchi, Mohammad Norouzi

On Accelerated Perceptrons and Beyond
Guanghui Wang, Rafael Hanashiro, Etash Guha, Jacob Abernethy

On Compositional Uncertainty Quantification for Seq2seq Graph Parsing
Zi Lin*, Du Phan, Panupong Pasupat, Jeremiah Liu, Jingbo Shang

On the Robustness of Safe Reinforcement Learning Under Observational Perturbations
Zuxin Liu, Zijian Guo, Zhepeng Cen, Huan Zhang, Jie Tan, Bo Li, Ding Zhao

Online Low Rank Matrix Completion
Prateek Jain, Soumyabrata Pal

Out-of-Distribution Detection and Selective Generation for Conditional Language Models
Jie Ren, Jiaming Luo, Yao Zhao, Kundan Krishna*, Mohammad Saleh, Balaji Lakshminarayanan, Peter J. Liu

PaLI: A Jointly-Scaled Multilingual Language-Image Model
Xi Chen, Xiao Wang, Soravit Changpinyo, AJ Piergiovanni, Piotr Padlewski, Daniel Salz, Sebastian Goodman, Adam Grycner, Basil Mustafa, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Nan Ding, Keran Rong, Hassan Akbari, Gaurav Mishra, Linting Xue, Ashish V. Thapliyal, James Bradbury, Weicheng Kuo, Mojtaba Seyedhosseini, Chao Jia, Burcu Karagol Ayan, Carlos Riquelme Ruiz, Andreas Peter Steiner, Anelia Angelova, Xiaohua Zhai, Neil Houlsby, Radu Soricut

Phenaki: Variable Length Video Generation from Open Domain Textual Descriptions
Ruben Villegas, Mohammad Babaeizadeh, Pieter-Jan Kindermans, Hernan Moraldo, Han Zhang, Mohammad Taghi Saffar, Santiago Castro*, Julius Kunze*, Dumitru Erhan

Promptagator: Few-Shot Dense Retrieval from 8 Examples
Zhuyun Dai, Vincent Y. Zhao, Ji Ma, Yi Luan, Jianmo Ni, Jing Lu, Anton Bakalov, Kelvin Guu, Keith B. Hall, Ming-Wei Chang

Pushing the Accuracy-Group Robustness Frontier with Introspective Self-Play
Jeremiah Zhe Liu, Krishnamurthy Dj Dvijotham, Jihyeon Lee, Quan Yuan, Balaji Lakshminarayanan, Deepak Ramachandran

Re-Imagen: Retrieval-Augmented Text-to-Image Generator Wenhu Chen, Hexiang Hu, Chitwan Saharia, William W. Cohen

Recitation-Augmented Language Models
Zhiqing Sun, Xuezhi Wang, Yi Tay, Yiming Yang, Denny Zhou

Regression with Label Differential Privacy
Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Avinash Varadarajan, Chiyuan Zhang

Revisiting the Entropy Semiring for Neural Speech Recognition
Oscar Chang, Dongseong Hwang, Olivier Siohan

Robust Active Distillation
Cenk Baykal, Khoa Trinh, Fotis Iliopoulos, Gaurav Menghani, Erik Vee

Score-Based Continuous-Time Discrete Diffusion Models
Haoran Sun*, Lijun Yu, Bo Dai, Dale Schuurmans, Hanjun Dai

Self-Consistency Improves Chain of Thought Reasoning in Language Models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, Denny Zhou

Self-Supervision Through Random Segments with Autoregressive Coding (RandSAC)
Tianyu Hua, Yonglong Tian, Sucheng Ren, Michalis Raptis, Hang Zhao, Leonid Sigal

Serving Graph Compression for Graph Neural Networks
Si Si, Felix Yu, Ankit Singh Rawat, Cho-Jui Hsieh, Sanjiv Kumar

Sequential Attention for Feature Selection
Taisuke Yasuda*, MohammadHossein Bateni, Lin Chen, Matthew Fahrbach, Gang Fu, Vahab Mirrokni

Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints
Aran Komatsuzaki*, Joan Puigcerver, James Lee-Thorp, Carlos Riquelme, Basil Mustafa, Joshua Ainslie, Yi Tay, Mostafa Dehghani, Neil Houlsby

Spectral Decomposition Representation for Reinforcement Learning
Tongzheng Ren, Tianjun Zhang, Lisa Lee, Joseph Gonzalez, Dale Schuurmans, Bo Dai

Spotlight: Mobile UI Understanding Using Vision-Language Models with a Focus (see blog post)
Gang Li, Yang Li

Supervision Complexity and Its Role in Knowledge Distillation
Hrayr Harutyunyan*, Ankit Singh Rawat, Aditya Krishna Menon, Seungyeon Kim, Sanjiv Kumar

Teacher Guided Training: An Efficient Framework for Knowledge Transfer
Manzil Zaheer, Ankit Singh Rawat, Seungyeon Kim, Chong You, Himanshu Jain, Andreas Veit, Rob Fergus, Sanjiv Kumar

TEMPERA: Test-Time Prompt Editing via Reinforcement Learning
Tianjun Zhang, Xuezhi Wang, Denny Zhou, Dale Schuurmans, Joseph E. Gonzalez

UL2: Unifying Language Learning Paradigms
Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Jason Wei, Xuezhi Wang, Hyung Won Chung, Dara Bahri, Tal Schuster, Steven Zheng, Denny Zhou, Neil Houlsby, Donald Metzler

* Work done while at Google

Source: Google AI Blog

Google at ICCV 2021

Posted by Cat Armato, Program Manager, Google Research

The International Conference on Computer Vision 2021 (ICCV 2021), one of the world's premier conferences on computer vision, starts this week. A Champion Sponsor and leader in computer vision research, Google will have a strong presence at ICCV 2021 with more than 50 research presentations and involvement in the organization of a number of workshops and tutorials.

If you are attending ICCV this year, we hope you’ll check out the work of our researchers who are actively pursuing the latest innovations in computer vision. Learn more about our research being presented in the list below (Google affilitation in bold).

Organizing Committee
Diversity and Inclusion Chair: Negar Rostamzadeh
Area Chairs: Andrea Tagliasacchi, Boqing Gong, Ce Liu, Dilip Krishnan, Jordi Pont-Tuset, Michael Rubinstein, Michael S. Ryoo, Negar Rostamzadeh, Noah Snavely, Rodrigo Benenson, Tsung-Yi Lin, Vittorio Ferrari

Publications
MosaicOS: A Simple and Effective Use of Object-Centric Images for Long-Tailed Object Detection
Cheng Zhang, Tai-Yu Pan, Yandong Li, Hexiang Hu, Dong Xuan, Soravit Changpinyo, Boqing Gong, Wei-Lun Chao

Learning to Resize Images for Computer Vision Tasks
Hossein Talebi, Peyman Milanfar

Joint Representation Learning and Novel Category Discovery on Single- and Multi-Modal Data
Xuhui Jia, Kai Han, Yukun Zhu, Bradley Green

Explaining in Style: Training a GAN to Explain a Classifier in StyleSpace
Oran Lang, Yossi Gandelsman, Michal Yarom, Yoav Wald, Gal Elidan, Avinatan Hassidim, William T. Freeman, Phillip Isola, Amir Globerson, Michal Irani, Inbar Mosseri

Learning Fast Sample Re-weighting without Reward Data
Zizhao Zhang, Tomas Pfister

Contrastive Multimodal Fusion with TupleInfoNCE
Yunze Liu, Qingnan Fan, Shanghang Zhang, Hao Dong, Thomas Funkhouser, Li Yi

Learning Temporal Dynamics from Cycles in Narrated Video
Dave Epstein*, Jiajun Wu, Cordelia Schmid, Chen Sun

Patch Craft: Video Denoising by Deep Modeling and Patch Matching
Gregory Vaksman, Michael Elad, Peyman Milanfar

How to Train Neural Networks for Flare Removal
Yicheng Wu*, Qiurui He, Tianfan Xue, Rahul Garg, Jiawen Chen, Ashok Veeraraghavan, Jonathan T. Barron

Learning to Reduce Defocus Blur by Realistically Modeling Dual-Pixel Data
Abdullah Abuolaim*, Mauricio Delbracio, Damien Kelly, Michael S. Brown, Peyman Milanfar

Hybrid Neural Fusion for Full-Frame Video Stabilization
Yu-Lun Liu, Wei-Sheng Lai, Ming-Hsuan Yang, Yung-Yu Chuang, Jia-Bin Huang

A Dark Flash Normal Camera
Zhihao Xia*, Jason Lawrence, Supreeth Achar

Efficient Large Scale Inlier Voting for Geometric Vision Problems
Dror Aiger, Simon Lynen, Jan Hosang, Bernhard Zeisl

Big Self-Supervised Models Advance Medical Image Classification
Shekoofeh Azizi, Basil Mustafa, Fiona Ryan*, Zachary Beaver, Jan Freyberg, Jonathan Deaton, Aaron Loh, Alan Karthikesalingam, Simon Kornblith, Ting Chen, Vivek Natarajan, Mohammad Norouzi

Physics-Enhanced Machine Learning for Virtual Fluorescence Microscopy
Colin L. Cooke, Fanjie Kong, Amey Chaware, Kevin C. Zhou, Kanghyun Kim, Rong Xu, D. Michael Ando, Samuel J. Yang, Pavan Chandra Konda, Roarke Horstmeyer

Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval
Min Jin Chong, Wen-Sheng Chu, Abhishek Kumar, David Forsyth

Deep Survival Analysis with Longitudinal X-Rays for COVID-19
Michelle Shu, Richard Strong Bowen, Charles Herrmann, Gengmo Qi, Michele Santacatterina, Ramin Zabih

MUSIQ: Multi-Scale Image Quality Transformer
Junjie Ke, Qifei Wang, Yilin Wang, Peyman Milanfar, Feng Yang

imGHUM: Implicit Generative Models of 3D Human Shape and Articulated Pose
Thiemo Alldieck, Hongyi Xu, Cristian Sminchisescu

Deep Hybrid Self-Prior for Full 3D Mesh Generation
Xingkui Wei, Zhengqing Chen, Yanwei Fu, Zhaopeng Cui, Yinda Zhang

Differentiable Surface Rendering via Non-Differentiable Sampling
Forrester Cole, Kyle Genova, Avneesh Sud, Daniel Vlasic, Zhoutong Zhang

A Lazy Approach to Long-Horizon Gradient-Based Meta-Learning
Muhammad Abdullah Jamal, Liqiang Wang, Boqing Gong

ViViT: A Video Vision Transformer
Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, Cordelia Schmid

The Surprising Impact of Mask-Head Architecture on Novel Class Segmentation (see the blog post)
Vighnesh Birodkar, Zhichao Lu, Siyang Li, Vivek Rathod, Jonathan Huang

Generalize Then Adapt: Source-Free Domain Adaptive Semantic Segmentation
Jogendra Nath Kundu, Akshay Kulkarni, Amit Singh, Varun Jampani, R. Venkatesh Babu

Unified Graph Structured Models for Video Understanding
Anurag Arnab, Chen Sun, Cordelia Schmid

The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, Justin Gilmer

Learning Rare Category Classifiers on a Tight Labeling Budget
Ravi Teja Mullapudi, Fait Poms, William R. Mark, Deva Ramanan, Kayvon Fatahalian

Composable Augmentation Encoding for Video Representation Learning
Chen Sun, Arsha Nagrani, Yonglong Tian, Cordelia Schmid

Multi-Task Self-Training for Learning General Representations
Golnaz Ghiasi, Barret Zoph, Ekin D. Cubuk, Quoc V. Le, Tsung-Yi Lin

With a Little Help From My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations
Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, Andrew Zisserman

Understanding Robustness of Transformers for Image Classification
Srinadh Bhojanapalli, Ayan Chakrabarti, Daniel Glasner, Daliang Li, Thomas Unterthiner, Andreas Veit

Impact of Aliasing on Generalization in Deep Convolutional Networks
Cristina Vasconcelos, Hugo Larochelle, Vincent Dumoulin, Rob Romijnders, Nicolas Le Roux, Ross Goroshin

von Mises-Fisher Loss: An Exploration of Embedding Geometries for Supervised Learning
Tyler R. Scott*, Andrew C. Gallagher, Michael C. Mozer

Contrastive Learning for Label Efficient Semantic Segmentation
Xiangyun Zhao*, Raviteja Vemulapalli, Philip Andrew Mansfield, Boqing Gong, Bradley Green, Lior Shapira, Ying Wu

Interacting Two-Hand 3D Pose and Shape Reconstruction from Single Color Image
Baowen Zhang, Yangang Wang, Xiaoming Deng, Yinda Zhang, Ping Tan, Cuixia Ma, Hongan Wang

Telling the What While Pointing to the Where: Multimodal Queries for Image Retrieval
Soravit Changpinyo, Jordi Pont-Tuset, Vittorio Ferrari, Radu Soricut

SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation
Yan Di, Fabian Manhardt, Gu Wang, Xiangyang Ji, Nassir Navab, Federico Tombari

Patch2CAD: Patchwise Embedding Learning for In-the-Wild Shape Retrieval from a Single Image
Weicheng Kuo, Anelia Angelova, Tsung-Yi Lin, Angela Dai

NeRD: Neural Reflectance Decomposition From Image Collections
Mark Boss, Raphael Braun, Varun Jampani, Jonathan T. Barron, Ce Liu, Hendrik P.A. Lensch

THUNDR: Transformer-Based 3D Human Reconstruction with Markers
Mihai Zanfir, Andrei Zanfir, Eduard Gabriel Bazavan, William T. Freeman, Rahul Sukthankar, Cristian Sminchisescu

Discovering 3D Parts from Image Collections
Chun-Han Yao, Wei-Chih Hung, Varun Jampani, Ming-Hsuan Yang

Multiresolution Deep Implicit Functions for 3D Shape Representation
Zhang Chen*, Yinda Zhang, Kyle Genova, Sean Fanello, Sofien Bouaziz, Christian Hane, Ruofei Du, Cem Keskin, Thomas Funkhouser, Danhang Tang

AI Choreographer: Music Conditioned 3D Dance Generation With AIST++ (see the blog post)
Ruilong Li*, Shan Yang, David A. Ross, Angjoo Kanazawa

Learning Object-Compositional Neural Radiance Field for Editable Scene Rendering
Bangbang Yang, Han Zhou, Yinda Zhang, Hujun Bao, Yinghao Xu, Guofeng Zhang, Yijin Li, Zhaopeng Cui

VariTex: Variational Neural Face Textures
Marcel C. Buhler, Abhimitra Meka, Gengyan Li, Thabo Beeler, Otmar Hilliges

Pathdreamer: A World Model for Indoor Navigation (see the blog post)
Jing Yu Koh, Honglak Lee, Yinfei Yang, Jason Baldridge, Peter Anderson

4D-Net for Learned Multi-Modal Alignment
AJ Piergiovanni, Vincent Casser, Michael S. Ryoo, Anelia Angelova

Episodic Transformer for Vision-and-Language Navigation
Alexander Pashevich*, Cordelia Schmid, Chen Sun

Graph-to-3D: End-to-End Generation and Manipulation of 3D Scenes Using Scene Graphs
Helisa Dhamo, Fabian Manhardt, Nassir Navab, Federico Tombari

Unconditional Scene Graph Generation
Sarthak Garg, Helisa Dhamo, Azade Farshad, Sabrina Musatian, Nassir Navab, Federico Tombari

Panoptic Narrative Grounding
Cristina González, Nicolás Ayobi, Isabela Hernández, José Hernández, Jordi Pont-Tuset, Pablo Arbeláez

Cross-Camera Convolutional Color Constancy
Mahmoud Afifi*, Jonathan T. Barron, Chloe LeGendre, Yun-Ta Tsai, Francois Bleibel

Defocus Map Estimation and Deblurring from a Single Dual-Pixel Image
Shumian Xin*, Neal Wadhwa, Tianfan Xue, Jonathan T. Barron, Pratul P. Srinivasan, Jiawen Chen, Ioannis Gkioulekas, Rahul Garg

COMISR: Compression-Informed Video Super-Resolution
Yinxiao Li, Pengchong Jin, Feng Yang, Ce Liu, Ming-Hsuan Yang, Peyman Milanfar

Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields
Jonathan T. Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, Pratul P. Srinivasan

Nerfies: Deformable Neural Radiance Fields
Keunhong Park*, Utkarsh Sinha, Jonathan T. Barron, Sofien Bouaziz, Dan B Goldman, Steven M. Seitz, Ricardo Martin-Brualla

Baking Neural Radiance Fields for Real-Time View Synthesis
Peter Hedman, Pratul P. Srinivasan, Ben Mildenhall, Jonathan T. Barron, Paul Debevec

Stacked Homography Transformations for Multi-View Pedestrian Detection
Liangchen Song, Jialian Wu, Ming Yang, Qian Zhang, Yuan Li, Junsong Yuan

COTR: Correspondence Transformer for Matching Across Images
Wei Jiang, Eduard Trulls, Jan Hosang, Andrea Tagliasacchi, Kwang Moo Yi

Large Scale Interactive Motion Forecasting for Autonomous Driving: The Waymo Open Motion Dataset
Scott Ettinger, Shuyang Cheng, Benjamin Caine, Chenxi Liu, Hang Zhao, Sabeek Pradhan, Yuning Chai, Ben Sapp, Charles R. Qi, Yin Zhou, Zoey Yang, Aurélien Chouard, Pei Sun, Jiquan Ngiam, Vijay Vasudevan, Alexander McCauley, Jonathon Shlens, Dragomir Anguelov

Low-Shot Validation: Active Importance Sampling for Estimating Classifier Performance on Rare Categories
Fait Poms, Vishnu Sarukkai, Ravi Teja Mullapudi, Nimit S. Sohoni, William R. Mark, Deva Ramanan, Kayvon Fatahalian

Vector Neurons: A General Framework for SO(3)-Equivariant Networks
Congyue Deng, Or Litany, Yueqi Duan, Adrien Poulenard, Andrea Tagliasacchi, Leonidas J. Guibas

SLIDE: Single Image 3D Photography with Soft Layering and Depth-Aware Inpainting
Varun Jampani, Huiwen Chang, Kyle Sargent, Abhishek Kar, Richard Tucker, Michael Krainin, Dominik Kaeser, William T. Freeman, David Salesin, Brian Curless, Ce Liu

DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context Graph and Relation-Based Optimization
Cheng Zhang, Zhaopeng Cui, Cai Chen, Shuaicheng Liu, Bing Zeng, Hujun Bao, Yinda Zhang

Infinite Nature: Perpetual View Generation of Natural Scenes from a Single Image
Andrew Liu, Richard Tucker, Varun Jampani, Ameesh Makadia, Noah Snavely, Angjoo Kanazawa

Workshops (only Google affiliations are noted)
Visual Inductive Priors for Data-Efficient Deep Learning Workshop
Speakers: Ekin Dogus Cubuk, Chelsea Finn

Instance-Level Recognition Workshop
Organizers: Andre Araujo, Cam Askew, Bingyi Cao, Jack Sim, Tobias Weyand

Unsup3D: Unsupervised 3D Learning in the Wild
Speakers: Adel Ahmadyan, Noah Snavely, Tali Dekel

Embedded and Real-World Computer Vision in Autonomous Driving (ERCVAD 2021)
Speakers: Mingxing Tan

Adversarial Robustness in the Real World
Speakers: Nicholas Carlini

Neural Architectures: Past, Present and Future
Speakers: Been Kim, Hanxiao Liu Organizers: Azade Nazi, Mingxing Tan, Quoc V. Le

Computational Challenges in Digital Pathology
Organizers: Craig Mermel, Po-Hsuan Cameron Chen

Interactive Labeling and Data Augmentation for Vision
Speakers: Vittorio Ferrari

Map-Based Localization for Autonomous Driving
Speakers: Simon Lynen

DeeperAction: Challenge and Workshop on Localized and Detailed Understanding of Human Actions in Videos
Speakers: Chen Sun Advisors: Rahul Sukthankar

Differentiable 3D Vision and Graphics
Speakers: Angjoo Kanazawa

Deep Multi-Task Learning in Computer Vision
Speakers: Chelsea Finn

Computer Vision for AR/VR
Speakers: Matthias Grundmann, Ira Kemelmacher-Shlizerman

GigaVision: When Gigapixel Videography Meets Computer Vision
Organizers: Feng Yang

Human Interaction for Robotic Navigation
Speakers: Peter Anderson

Advances in Image Manipulation Workshop and Challenges
Organizers: Ming-Hsuan Yang

More Exploration, Less Exploitation (MELEX)
Speakers: Angjoo Kanazawa

Structural and Compositional Learning on 3D Data
Speakers: Thomas Funkhouser, Kyle Genova Organizers: Fei Xia

Simulation Technology for Embodied AI
Organizers: Li Yi

Video Scene Parsing in the Wild Challenge Workshop
Speakers: Liang-Chieh (Jay) Chen

Structured Representations for Video Understanding
Organizers: Cordelia Schmid

Closing the Loop Between Vision and Language
Speakers: Cordelia Schmid

Segmenting and Tracking Every Point and Pixel: 6th Workshop on Benchmarking Multi-Target Tracking
Organizers: Jun Xie, Liang-Chieh Chen

AI for Creative Video Editing and Understanding
Speakers: Angjoo Kanazawa, Irfan Essa

BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environments
Speakers: Chelsea Finn Organizers: Fei Xia

Computer Vision for Automated Medical Diagnosis
Organizers: Maithra Raghu

Computer Vision for the Factory Floor
Speakers: Cordelia Schmid

Tutorials (only Google affiliations are noted)
Towards Robust, Trustworthy, and Explainable Computer Vision
Speakers: Sara Hooker

Multi-Modality Learning from Videos and Beyond
Organizers: Arsha Nagrani

Tutorial on Large Scale Holistic Video Understanding
Organizers: David Ross

Efficient Video Understanding: State of the Art, Challenges, and Opportunities
Organizers: Arsha Nagrani

* Indicates work done while at Google

Source: Google AI Blog

Google at ICML 2021

Posted by Cat Armato and Jaqui Herman, Program Managers

Groups across Google are actively pursuing research across the field of machine learning, ranging from theory to application. With scalable tools and architectures, we build machine learning systems to solve deep scientific and engineering challenges in areas of language, music, visual processing, and more.

Google is proud to be a Platinum Sponsor of the thirty-eighth International Conference on Machine Learning (ICML 2021), a premier annual event happening this week. As a leader in machine learning research — with over 100 accepted publications and Googlers participating in workshops — we look forward to our continued partnership with the broader machine learning research community.

Registered for ICML 2021? We hope you’ll visit the Google virtual booth to learn more about the exciting work, creativity, and fun that goes into solving a portion of the field’s most interesting challenges. Take a look below to learn more about the Google research being presented at ICML 2021 (Google affiliations in bold).

Organizing Committee
ICML Board Members include: Corinna Cortes, Hugo Larochelle, Shakir Mohamed
ICML Emeritus Board includes: William Cohen, Andrew McCallum
Tutorial Co-Chair member: Quoc Lee

Publications
Attention Is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth
Yihe Dong, Jean-Baptiste Cordonnier, Andreas Loukas

Scalable Evaluation of Multi-agent Reinforcement Learning with Melting Pot
Joel Z. Leibo, Edgar Duéñez-Guzmán, Alexander Sasha Vezhnevets, John P. Agapiou, Peter Sunehag, Raphael Koster, Jayd Matyas, Charles Beattie, Igor Mordatch, Thore Graepel

On the Optimality of Batch Policy Optimization Algorithms
Chenjun Xiao, Yifan Wu, Tor Lattimore, Bo Dai, Jincheng Mei, Lihong Li*, Csaba Szepesvari, Dale Schuurmans

Low-Rank Sinkhorn Factorization
Meyer Scetbon, Marco Cuturi, Gabriel Peyré

Oops I Took A Gradient: Scalable Sampling for Discrete Distributions
Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison

PID Accelerated Value Iteration Algorithm
Amir-Massoud Farahmand, Mohammad Ghavamzadeh

Dueling Convex Optimization
Aadirupa Saha, Tomer Koren, Yishay Mansour

What Are Bayesian Neural Network Posteriors Really Like?
Pavel Izmailov, Sharad Vikram, Matthew D. Hoffman, Andrew Gordon Wilson

Offline Reinforcement Learning with Pseudometric Learning
Robert Dadashi, Shideh Rezaeifar, Nino Vieillard, Léonard Hussenot, Olivier Pietquin, Matthieu Geist

Revisiting Rainbow: Promoting More Insightful and Inclusive Deep Reinforcement Learning Research (see blog post)
Johan S. Obando-Ceron, Pablo Samuel Castro

EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL
Seyed Kamyar Seyed Ghasemipour*, Dale Schuurmans, Shixiang Shane Gu

Variational Data Assimilation with a Learned Inverse Observation Operator
Thomas Frerix, Dmitrii Kochkov, Jamie A. Smith, Daniel Cremers, Michael P. Brenner, Stephan Hoyer

Tilting the Playing Field: Dynamical Loss Functions for Machine Learning
Miguel Ruiz-Garcia, Ge Zhang, Samuel S. Schoenholz, Andrea J. Liu

Model-Based Reinforcement Learning via Latent-Space Collocation
Oleh Rybkin, Chuning Zhu, Anusha Nagabandi, Kostas Daniilidis, Igor Mordatch, Sergey Levine

Momentum Residual Neural Networks
Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré

OmniNet: Omnidirectional Representations from Transformers
Yi Tay, Mostafa Dehghani, Vamsi Aribandi, Jai Gupta, Philip Pham, Zhen Qin, Dara Bahri, Da-Cheng Juan, Donald Metzler

Synthesizer: Rethinking Self-Attention for Transformer Models
Yi Tay, Dara Bahri, Donald Metzler, Da-Cheng Juan, Zhe Zhao, Che Zheng

Towards Domain-Agnostic Contrastive Learning
Vikas Verma, Minh-Thang Luong, Kenji Kawaguchi, Hieu Pham, Quoc V. Le

Randomized Entity-wise Factorization for Multi-agent Reinforcement Learning
Shariq Iqbal, Christian A. Schroeder de Witt, Bei Peng, Wendelin Böhmer, Shimon Whiteson, Fei Sha

LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning
Yuhuai Wu, Markus Rabe, Wenda Li, Jimmy Ba, Roger Grosse, Christian Szegedy

Emergent Social Learning via Multi-agent Reinforcement Learning
Kamal Ndousse, Douglas Eck, Sergey Levine, Natasha Jaques

Improved Contrastive Divergence Training of Energy-Based Models
Yilun Du, Shuang Li, Joshua Tenenbaum, Igor Mordatch

Characterizing Structural Regularities of Labeled Data in Overparameterized Models
Ziheng Jiang*, Chiyuan Zhang, Kunal Talwar, Michael Mozer

Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills
Yevgen Chebotar, Karol Hausman, Yao Lu, Ted Xiao, Dmitry Kalashnikov, Jake Varley, Alex Irpan, Benjamin Eysenbach, Ryan Julian, Chelsea Finn, Sergey Levine

PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning
Angelos Filos, Clare Lyle, Yarin Gal, Sergey Levine, Natasha Jaques, Gregory Farquhar

EfficientNetV2: Smaller Models and Faster Training
Mingxing Tan, Quoc V. Le

Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies
Paul Vicol, Luke Metz, Jascha Sohl-Dickstein

Federated Composite Optimization
Honglin Yuan*, Manzil Zaheer, Sashank Reddi

Light RUMs
Flavio Chierichetti, Ravi Kumar, Andrew Tomkins

Catformer: Designing Stable Transformers via Sensitivity Analysis
Jared Quincy Davis, Albert Gu, Krzysztof Choromanski, Tri Dao, Christopher Re, Chelsea Finn, Percy Liang

Representation Matters: Offline Pretraining for Sequential Decision Making
Mengjiao Yang, Ofir Nachum

Variational Empowerment as Representation Learning for Goal-Conditioned Reinforcement Learning
Jongwook Choi*, Archit Sharma*, Honglak Lee, Sergey Levine, Shixiang Shane Gu

Beyond Variance Reduction: Understanding the True Impact of Baselines on Policy Optimization
Wesley Chung, Valentin Thomas, Marlos C. Machado, Nicolas Le Roux

Whitening and Second Order Optimization Both Make Information in the Dataset Unusable During Training, and Can Reduce or Prevent Generalization
Neha S. Wadia, Daniel Duckworth, Samuel S. Schoenholz, Ethan Dyer, Jascha Sohl-Dickstein

Understanding Invariance via Feedforward Inversion of Discriminatively Trained Classifiers
Piotr Teterwak*, Chiyuan Zhang, Dilip Krishnan, Michael C. Mozer

Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning
Hiroki Furuta, Tatsuya Matsushima, Tadashi Kozuno, Yutaka Matsuo, Sergey Levine, Ofir Nachum, Shixiang Shane Gu

Hyperparameter Selection for Imitation Learning
Leonard Hussenot, Marcin Andrychowicz, Damien Vincent, Robert Dadashi, Anton Raichuk, Lukasz Stafiniak, Sertan Girgin, Raphael Marinier, Nikola Momchev, Sabela Ramos, Manu Orsini, Olivier Bachem, Matthieu Geist, Olivier Pietquin

Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces
Ankit Singh Rawat, Aditya Krishna Menon, Wittawat Jitkrittum, Sadeep Jayasumana, Felix X. Yu, Sashank J. Reddi, Sanjiv Kumar

Revenue-Incentive Tradeoffs in Dynamic Reserve Pricing
Yuan Deng, Sebastien Lahaie, Vahab Mirrokni, Song Zuo

Debiasing a First-Order Heuristic for Approximate Bi-Level Optimization
Valerii Likhosherstov, Xingyou Song, Krzysztof Choromanski, Jared Davis, Adrian Weller

Characterizing the Gap Between Actor-Critic and Policy Gradient
Junfeng Wen, Saurabh Kumar, Ramki Gummadi, Dale Schuurmans

Composing Normalizing Flows for Inverse Problems
Jay Whang, Erik Lindgren, Alexandros Dimakis

Online Policy Gradient for Model Free Learning of Linear Quadratic Regulators with √T Regret
Asaf Cassel, Tomer Koren

Learning to Price Against a Moving Target
Renato Paes Leme, Balasubramanian Sivan, Yifeng Teng, Pratik Worah

Fairness and Bias in Online Selection
Jose Correa, Andres Cristi, Paul Duetting, Ashkan Norouzi-Fard

The Impact of Record Linkage on Learning from Feature Partitioned Data
Richard Nock, Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Jakub Nabaglo, Giorgio Patrini, Guillaume Smith, Brian Thorne

Reserve Price Optimization for First Price Auctions in Display Advertising
Zhe Feng*, Sébastien Lahaie, Jon Schneider, Jinchao Ye

A Regret Minimization Approach to Iterative Learning Control
Naman Agarwal, Elad Hazan, Anirudha Majumdar, Karan Singh

A Statistical Perspective on Distillation
Aditya Krishna Menon, Ankit Singh Rawat, Sashank J. Reddi, Seungyeon Kim, Sanjiv Kumar

Best Model Identification: A Rested Bandit Formulation
Leonardo Cella, Massimiliano Pontil, Claudio Gentile

Generalised Lipschitz Regularisation Equals Distributional Robustness
Zac Cranko, Zhan Shi, Xinhua Zhang, Richard Nock, Simon Kornblith

Stochastic Multi-armed Bandits with Unrestricted Delay Distributions
Tal Lancewicki, Shahar Segal, Tomer Koren, Yishay Mansour

Regularized Online Allocation Problems: Fairness and Beyond
Santiago Balseiro, Haihao Lu, Vahab Mirrokni

Implicit Rate-Constrained Optimization of Non-decomposable Objectives
Abhishek Kumar, Harikrishna Narasimhan, Andrew Cotter

Leveraging Non-uniformity in First-Order Non-Convex Optimization
Jincheng Mei, Yue Gao, Bo Dai, Csaba Szepesvari, Dale Schuurmans

Dynamic Balancing for Model Selection in Bandits and RL
Ashok Cutkosky, Christoph Dann, Abhimanyu Das, Claudio Gentile, Aldo Pacchiano, Manish Purohit

Adversarial Dueling Bandits
Aadirupa Saha, Tomer Koren, Yishay Mansour

Optimizing Black-Box Metrics with Iterative Example Weighting
Gaurush Hiranandani*, Jatin Mathur, Harikrishna Narasimhan, Mahdi Milani Fard, Oluwasanmi Koyejo

Relative Deviation Margin Bounds
Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh

MC-LSTM: Mass-Conserving LSTM
Pieter-Jan Hoedt, Frederik Kratzert, Daniel Klotz, Christina Halmich, Markus Holzleitner, Grey Nearing, Sepp Hochreiter, Günter Klambauer

12-Lead ECG Reconstruction via Koopman Operators
Authors:Tomer Golany, Kira Radinsky, Daniel Freedman, Saar Minha

Finding Relevant Information via a Discrete Fourier Expansion
Mohsen Heidari, Jithin Sreedharan, Gil Shamir, Wojciech Szpankowski

LEGO: Latent Execution-Guided Reasoning for Multi-hop Question Answering on Knowledge Graphs
Hongyu Ren, Hanjun Dai, Bo Dai, Xinyun Chen, Michihiro Yasunaga, Haitian Sun, Dale Schuurmans, Jure Leskovec, Denny Zhou

SpreadsheetCoder: Formula Prediction from Semi-structured Context
Xinyun Chen, Petros Maniatis, Rishabh Singh, Charles Sutton, Hanjun Dai, Max Lin, Denny Zhou

Combinatorial Blocking Bandits with Stochastic Delays
Alexia Atsidakou, Orestis Papadigenopoulos, Soumya Basu, Constantine Caramani, Sanjay Shakkottai

Beyond log2(T) Regret for Decentralized Bandits in Matching Markets
Soumya Basu, Karthik Abinav Sankararaman, Abishek Sankararaman

Robust Pure Exploration in Linear Bandits with Limited Budget
Ayya Alieva, Ashok Cutkosky, Abhimanyu Das

Latent Programmer: Discrete Latent Codes for Program Synthesis
Joey Hong, David Dohan, Rishabh Singh, Charles Sutton, Manzil Zaheer

Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision (see blog post)
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, Tom Duerig

On Linear Identifiability of Learned Representations
Geoffrey Roeder, Luke Metz, Diederik P. Kingma

Hierarchical Clustering of Data Streams: Scalable Algorithms and Approximation Guarantees
Anand Rajagopalan, Fabio Vitale, Danny Vainstein, Gui Citovsky, Cecilia M Procopiuc, Claudio Gentile

Differentially Private Quantiles
Jennifer Gillenwater, Matthew Joseph, Alex Kulesza

Active Covering
Heinrich Jiang, Afshin Rostamizadeh

Sharf: Shape-Conditioned Radiance Fields from a Single View
Konstantinos Rematas, Ricardo Martin-Brualla, Vittorio Ferrari

Learning a Universal Template for Few-Shot Dataset Generalization
Eleni Triantafillou*, Hugo Larochelle, Richard Zemel, Vincent Dumoulin

Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates
Steve Chien, Prateek Jain, Walid Krichene, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang

Differentially-Private Clustering of Easy Instances
Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia

Label-Only Membership Inference Attacks
Christopher A. Choquette-Choo, Florian Tramèr, Nicholas Carlini, Nicolas Papernot

Neural Feature Matching in Implicit 3D Representations
Yunlu Chen, Basura Fernando, Hakan Bilen, Thomas Mensink, Efstratios Gavves

Locally Private k-Means in One Round
Alisa Chang, Badih Ghazi, Ravi Kumar, Pasin Manurangsi

Large-Scale Meta-learning with Continual Trajectory Shifting
Jaewoong Shin, Hae Beom Lee, Boqing Gong, Sung Ju Hwang

Statistical Estimation from Dependent Data
Vardis Kandiros, Yuval Dagan, Nishanth Dikkala, Surbhi Goel, Constantinos Daskalakis

Oneshot Differentially Private Top-k Selection
Gang Qiao, Weijie J. Su, Li Zhang

Unsupervised Part Representation by Flow Capsules
Sara Sabour, Andrea Tagliasacchi, Soroosh Yazdani, Geoffrey E. Hinton, David J. Fleet

Private Stochastic Convex Optimization: Optimal Rates in L1 Geometry
Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar

Practical and Private (Deep) Learning Without Sampling or Shuffling
Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu

Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message
Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, Amer Sinha

Leveraging Public Data for Practical Private Query Release
Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan Ullman, Zhiwei Steven Wu

Meta-Thompson Sampling
Branislav Kveton, Mikhail Konobeev, Manzil Zaheer, Chih-wei Hsu, Martin Mladenov, Craig Boutilier, Csaba Szepesvári

Implicit-PDF: Non-parametric Representation of Probability Distributions on the Rotation Manifold
Kieran A Murphy, Carlos Esteves, Varun Jampani, Srikumar Ramalingam, Ameesh Makadia

Improving Ultrametrics Embeddings Through Coresets
Vincent Cohen-Addad, Rémi de Joannis de Verclos, Guillaume Lagarde

A Discriminative Technique for Multiple-Source Adaptation
Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh, Ningshan Zhang

Self-Supervised and Supervised Joint Training for Resource-Rich Machine Translation
Yong Cheng, Wei Wang*, Lu Jiang, Wolfgang Macherey

Correlation Clustering in Constant Many Parallel Rounds
Vincent Cohen-Addad, Silvio Lattanzi, Slobodan Mitrović, Ashkan Norouzi-Fard, Nikos Parotsidis, Jakub Tarnawski

Hierarchical Agglomerative Graph Clustering in Nearly-Linear Time
Laxman Dhulipala, David Eisenstat, Jakub Łącki, Vahab Mirrokni, Jessica Shi

Meta-learning Bidirectional Update Rules
Mark Sandler, Max Vladymyrov, Andrey Zhmoginov, Nolan Miller, Andrew Jackson, Tom Madams, Blaise Aguera y Arcas

Discretization Drift in Two-Player Games
Mihaela Rosca, Yan Wu, Benoit Dherin, David G.T. Barrett

Reasoning Over Virtual Knowledge Bases With Open Predicate Relations
Haitian Sun*, Pat Verga, Bhuwan Dhingra, Ruslan Salakhutdinov, William W. Cohen

Learn2Hop: Learned Optimization on Rough Landscapes
Amil Merchant, Luke Metz, Samuel Schoenholz, Ekin Cubuk

Locally Adaptive Label Smoothing Improves Predictive Churn
Dara Bahri, Heinrich Jiang

Overcoming Catastrophic Forgetting by Bayesian Generative Regularization
Patrick H. Chen, Wei Wei, Cho-jui Hsieh, Bo Dai

Workshops (only Google affiliations are noted)
LatinX in AI (LXAI) Research at ICML 2021
Hosts: Been Kim, Natasha Jaques

Uncertainty and Robustness in Deep Learning
Organizers: Balaji Lakshminarayanan, Jasper Snoek Invited Speaker: Dustin Tran

Reinforcement Learning for Real Life
Organizers: Minmin Chen, Lihong Li Invited Speaker: Ed Chi

Interpretable Machine Learning in Healthcare
Organizers: Alan Karthikesalingam Invited Speakers: Abhijit Guha Roy, Jim Winkens

The Neglected Assumptions in Causal Inference
Organizer: Alexander D'Amour

ICML Workshop on Algorithmic Recourse
Invited Speakers: Been Kim, Berk Ustun

A Blessing in Disguise: The Prospects and Perils of Adversarial Machine Learning
Invited Speaker: Nicholas Carlini

Overparameterization: Pitfalls and Opportunities
Organizers: Yasaman Bahri, Hanie Sedghi

Information-Theoretic Methods for Rigorous, Responsible, and Reliable Machine Learning (ITR3)
Invited Speaker: Thomas Steinke

Beyond First-Order Methods in Machine Learning Systems
Invited Speaker: Courtney Paquette

ICML 2021 Workshop: Self-Supervised Learning for Reasoning and Perception
Invited Speaker: Chelsea Finn

Workshop on Reinforcement Learning Theory
Invited Speaker: Bo Dai

Tutorials (only Google affiliations are noted)
Responsible AI in Industry: Practical Challenges and Lessons Learned
Organizers: Ben Packer

Online and Non-stochastic Control
Organizers: Elad Hazan

Random Matrix Theory and ML (RMT +ML)
Organizers: Fabian Pedregosa, Jeffrey Pennington, Courntey Paquette Self-Attention for Computer Vision Organizers: Prajit Ramachandran, Ashish Vaswani

* Indicates work done while at Google

Source: Google AI Blog

Google at CVPR 2021

Posted by Emily Knapp and Tim Herrmann, Program Managers

This week marks the start of the 2021 Conference on Computer Vision and Pattern Recognition (CVPR 2021), the premier annual computer vision event consisting of the main conference, workshops and tutorials. As a leader in computer vision research and a Champion Level Sponsor, Google will have a strong presence at CVPR 2021, with over 70 publications accepted, along with the organization of and participation in multiple workshops and tutorials.

If you are participating in CVPR this year, please visit our virtual booth to learn about Google research into 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 2021 in the list below (Google affiliations in bold).

Organizing Committee Members

General Chair: Rahul Sukthankar
Finance Chair: Ramin Zabih
Workshop Chair: Caroline Pantofaru
Area Chairs: Chen Sun, Golnaz Ghiasi, Jonathan Barron, Kostas Rematas, Negar Rostamzadeh, Noah Snavely, Sanmi Koyejo, Tsung-Yi Lin

Publications

Cross-Modal Contrastive Learning for Text-to-Image Generation (see the blog post)
Han Zhang, Jing Yu Koh, Jason Baldridge, Honglak Lee*, Yinfei Yang

Learning Graph Embeddings for Compositional Zero-Shot Learning
Muhammad Ferjad Naeem, Yongqin Xian, Federico Tombari, Zeynep Akata

SPSG: Self-Supervised Photometric Scene Generation From RGB-D Scans
Angela Dai, Yawar Siddiqui, Justus Thies, Julien Valentin, Matthias Nießner

3D-MAN: 3D Multi-Frame Attention Network for Object Detection
Zetong Yang*, Yin Zhou, Zhifeng Chen, Jiquan Ngiam

MIST: Multiple Instance Spatial Transformer
Baptiste Angles, Yuhe Jin, Simon Kornblith, Andrea Tagliasacchi, Kwang Moo Yi

OCONet: Image Extrapolation by Object Completion
Richard Strong Bowen*, Huiwen Chang, Charles Herrmann*, Piotr Teterwak*, Ce Liu, Ramin Zabih

Ranking Neural Checkpoints
Yandong Li, Xuhui Jia, Ruoxin Sang, Yukun Zhu, Bradley Green, Liqiang Wang, Boqing Gong

LipSync3D: Data-Efficient Learning of Personalized 3D Talking Faces From Video Using Pose and Lighting Normalization
Avisek Lahiri, Vivek Kwatra, Christian Frueh, John Lewis, Chris Bregler

Differentiable Patch Selection for Image Recognition
Jean-Baptiste Cordonnier*, Aravindh Mahendran, Alexey Dosovitskiy, Dirk Weissenborn, Jakob Uszkoreit, Thomas Unterthiner

HumanGPS: Geodesic PreServing Feature for Dense Human Correspondences
Feitong Tan, Danhang Tang, Mingsong Dou, Kaiwen Guo, Rohit Pandey, Cem Keskin, Ruofei Du, Deqing Sun, Sofien Bouaziz, Sean Fanello, Ping Tan, Yinda Zhang

VIP-DeepLab: Learning Visual Perception With Depth-Aware Video Panoptic Segmentation (see the blog post)
Siyuan Qiao*, Yukun Zhu, Hartwig Adam, Alan Yuille, Liang-Chieh Chen

DeFMO: Deblurring and Shape Recovery of Fast Moving Objects
Denys Rozumnyi, Martin R. Oswald, Vittorio Ferrari, Jiri Matas, Marc Pollefeys

HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps
Lu Mi, Hang Zhao, Charlie Nash, Xiaohan Jin, Jiyang Gao, Chen Sun, Cordelia Schmid, Nir Shavit, Yuning Chai, Dragomir Anguelov

Wide-Baseline Relative Camera Pose Estimation With Directional Learning
Kefan Chen, Noah Snavely, Ameesh Makadia

MobileDets: Searching for Object Detection Architectures for Mobile Accelerators
Yunyang Xiong, Hanxiao Liu, Suyog Gupta, Berkin Akin, Gabriel Bender, Yongzhe Wang, Pieter-Jan Kindermans, Mingxing Tan, Vikas Singh, Bo Chen

SMURF: Self-Teaching Multi-Frame Unsupervised RAFT With Full-Image Warping
Austin Stone, Daniel Maurer, Alper Ayvaci, Anelia Angelova, Rico Jonschkowski

Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts
Soravit Changpinyo, Piyush Sharma, Nan Ding, Radu Soricut

Uncalibrated Neural Inverse Rendering for Photometric Stereo of General Surfaces
Berk Kaya, Suryansh Kumar, Carlos Oliveira, Vittorio Ferrari, Luc Van Gool

MeanShift++: Extremely Fast Mode-Seeking With Applications to Segmentation and Object Tracking
Jennifer Jang, Heinrich Jiang

Repopulating Street Scenes
Yifan Wang*, Andrew Liu, Richard Tucker, Jiajun Wu, Brian L. Curless, Steven M. Seitz, Noah Snavely

MaX-DeepLab: End-to-End Panoptic Segmentation With Mask Transformers (see the blog post)
Huiyu Wang*, Yukun Zhu, Hartwig Adam, Alan Yuille, Liang-Chieh Chen

IBRNet: Learning Multi-View Image-Based Rendering
Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul Srinivasan, Howard Zhou, Jonathan T. Barron, Ricardo Martin-Brualla, Noah Snavely, Thomas Funkhouser

From Points to Multi-Object 3D Reconstruction
Francis Engelmann*, Konstantinos Rematas, Bastian Leibe, Vittorio Ferrari

Learning Compositional Representation for 4D Captures With Neural ODE
Boyan Jiang, Yinda Zhang, Xingkui Wei, Xiangyang Xue, Yanwei Fu

Guided Integrated Gradients: An Adaptive Path Method for Removing Noise
Andrei Kapishnikov, Subhashini Venugopalan, Besim Avci, Ben Wedin, Michael Terry, Tolga Bolukbasi

De-Rendering the World’s Revolutionary Artefacts
Shangzhe Wu*, Ameesh Makadia, Jiajun Wu, Noah Snavely, Richard Tucker, Angjoo Kanazawa

Spatiotemporal Contrastive Video Representation Learning
Rui Qian, Tianjian Meng, Boqing Gong, Ming-Hsuan Yang, Huisheng Wang, Serge Belongie, Yin Cui

Decoupled Dynamic Filter Networks
Jingkai Zhou, Varun Jampani, Zhixiong Pi, Qiong Liu, Ming-Hsuan Yang

NeuralHumanFVV: Real-Time Neural Volumetric Human Performance Rendering Using RGB Cameras
Xin Suo, Yuheng Jiang, Pei Lin, Yingliang Zhang, Kaiwen Guo, Minye Wu, Lan Xu

Regularizing Generative Adversarial Networks Under Limited Data
Hung-Yu Tseng*, Lu Jiang, Ce Liu, Ming-Hsuan Yang, Weilong Yang

SceneGraphFusion: Incremental 3D Scene Graph Prediction From RGB-D Sequences
Shun-Cheng Wu, Johanna Wald, Keisuke Tateno, Nassir Navab, Federico Tombari

NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis
Pratul P. Srinivasan, Boyang Deng, Xiuming Zhang, Matthew Tancik, Ben Mildenhall, Jonathan T. Barron

Adversarially Adaptive Normalization for Single Domain Generalization
Xinjie Fan*, Qifei Wang, Junjie Ke, Feng Yang, Boqing Gong, Mingyuan Zhou

Adaptive Prototype Learning and Allocation for Few-Shot Segmentation
Gen Li, Varun Jampani, Laura Sevilla-Lara, Deqing Sun, Jonghyun Kim, Joongkyu Kim

Adversarial Robustness Across Representation Spaces
Pranjal Awasthi, George Yu, Chun-Sung Ferng, Andrew Tomkins, Da-Cheng Juan

Background Splitting: Finding Rare Classes in a Sea of Background
Ravi Teja Mullapudi, Fait Poms, William R. Mark, Deva Ramanan, Kayvon Fatahalian

Searching for Fast Model Families on Datacenter Accelerators
Sheng Li, Mingxing Tan, Ruoming Pang, Andrew Li, Liqun Cheng, Quoc Le, Norman P. Jouppi

Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild With Pose Annotations (see the blog post)
Adel Ahmadyan, Liangkai Zhang, Jianing Wei, Artsiom Ablavatski, Matthias Grundmann

CutPaste: Self-Supervised Learning for Anomaly Detection and Localization
Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, Tomas Pfister

Nutrition5k: Towards Automatic Nutritional Understanding of Generic Food
Quin Thames, Arjun Karpur, Wade Norris, Fangting Xia, Liviu Panait, Tobias Weyand, Jack Sim

CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning
Chen Wei*, Kihyuk Sohn, Clayton Mellina, Alan Yuille, Fan Yang

DetectoRS: Detecting Objects With Recursive Feature Pyramid and Switchable Atrous Convolution
Siyuan Qiao, Liang-Chieh Chen, Alan Yuille

DeRF: Decomposed Radiance Fields
Daniel Rebain, Wei Jiang, Soroosh Yazdani, Ke Li, Kwang Moo Yi, Andrea Tagliasacchi

Variational Transformer Networks for Layout Generation (see the blog post)
Diego Martin Arroyo, Janis Postels, Federico Tombari

Rich Features for Perceptual Quality Assessment of UGC Videos
Yilin Wang, Junjie Ke, Hossein Talebi, Joong Gon Yim, Neil Birkbeck, Balu Adsumilli, Peyman Milanfar, Feng Yang

Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds
Li Yi, Boqing Gong, Thomas Funkhouser

Neural Descent for Visual 3D Human Pose and Shape
Andrei Zanfir, Eduard Gabriel Bazavan, Mihai Zanfir, William T. Freeman, Rahul Sukthankar, Cristian Sminchisescu

GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation
Gu Wang, Fabian Manhardt, Federico Tombari, Xiangyang Ji

Look Before You Speak: Visually Contextualized Utterances
Paul Hongsuck Seo, Arsha Nagrani, Cordelia Schmid

LASR: Learning Articulated Shape Reconstruction From a Monocular Video
Gengshan Yang*, Deqing Sun, Varun Jampani, Daniel Vlasic, Forrester Cole, Huiwen Chang, Deva Ramanan, William T. Freeman, Ce Liu

MoViNets: Mobile Video Networks for Efficient Video Recognition
Dan Kondratyuk, Liangzhe Yuan, Yandong Li, Li Zhang, Mingxing Tan, Matthew Brown, Boqing Gong

No Shadow Left Behind: Removing Objects and Their Shadows Using Approximate Lighting and Geometry
Edward Zhang, Ricardo Martin-Brualla, Janne Kontkanen, Brian Curless

On Robustness and Transferability of Convolutional Neural Networks
Josip Djolonga, Jessica Yung, Michael Tschannen, Rob Romijnders, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Matthias Minderer, Alexander D'Amour, Dan Moldovan, Sylvain Gelly, Neil Houlsby, Xiaohua Zhai, Mario Lucic

Robust and Accurate Object Detection via Adversarial Learning
Xiangning Chen, Cihang Xie, Mingxing Tan, Li Zhang, Cho-Jui Hsieh, Boqing Gong

To the Point: Efficient 3D Object Detection in the Range Image With Graph Convolution Kernels
Yuning Chai, Pei Sun, Jiquan Ngiam, Weiyue Wang, Benjamin Caine, Vijay Vasudevan, Xiao Zhang, Dragomir Anguelov

Bottleneck Transformers for Visual Recognition
Aravind Srinivas, Tsung-Yi Lin, Niki Parmar, Jonathon Shlens, Pieter Abbeel, Ashish Vaswani

Faster Meta Update Strategy for Noise-Robust Deep Learning
Youjiang Xu, Linchao Zhu, Lu Jiang, Yi Yang

Correlated Input-Dependent Label Noise in Large-Scale Image Classification
Mark Collier, Basil Mustafa, Efi Kokiopoulou, Rodolphe Jenatton, Jesse Berent

Learned Initializations for Optimizing Coordinate-Based Neural Representations
Matthew Tancik, Ben Mildenhall, Terrance Wang, Divi Schmidt, Pratul P. Srinivasan, Jonathan T. Barron, Ren Ng

Simple Copy-Paste Is a Strong Data Augmentation Method for Instance Segmentation
Golnaz Ghiasi, Yin Cui, Aravind Srinivas*, Rui Qian, Tsung-Yi Lin, Ekin D. Cubuk, Quoc V. Le, Barret Zoph

Function4D: Real-Time Human Volumetric Capture From Very Sparse Consumer RGBD Sensors
Tao Yu, Zerong Zheng, Kaiwen Guo, Pengpeng Liu, Qionghai Dai, Yebin Liu

RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection
Pei Sun, Weiyue Wang, Yuning Chai, Gamaleldin Elsayed, Alex Bewley, Xiao Zhang, Cristian Sminchisescu, Dragomir Anguelov

NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections
Ricardo Martin-Brualla, Noha Radwan, Mehdi S. M. Sajjadi, Jonathan T. Barron, Alexey Dosovitskiy, Daniel Duckworth

Robust Neural Routing Through Space Partitions for Camera Relocalization in Dynamic Indoor Environments
Siyan Dong, Qingnan Fan, He Wang, Ji Shi, Li Yi, Thomas Funkhouser, Baoquan Chen, Leonidas Guibas

Taskology: Utilizing Task Relations at Scale
Yao Lu, Sören Pirk, Jan Dlabal, Anthony Brohan, Ankita Pasad*, Zhao Chen, Vincent Casser, Anelia Angelova, Ariel Gordon

Omnimatte: Associating Objects and Their Effects in Video
Erika Lu, Forrester Cole, Tali Dekel, Andrew Zisserman, William T. Freeman, Michael Rubinstein

AutoFlow: Learning a Better Training Set for Optical Flow
Deqing Sun, Daniel Vlasic, Charles Herrmann, Varun Jampani, Michael Krainin, Huiwen Chang, Ramin Zabih, William T. Freeman, and Ce Liu

Unsupervised Multi-Source Domain Adaptation Without Access to Source Data
Sk Miraj Ahmed, Dripta S. Raychaudhuri, Sujoy Paul, Samet Oymak, Amit K. Roy-Chowdhury

Meta Pseudo Labels
Hieu Pham, Zihang Dai, Qizhe Xie, Minh-Thang Luong, Quoc V. Le

Spatially-Varying Outdoor Lighting Estimation From Intrinsics
Yongjie Zhu, Yinda Zhang, Si Li, Boxin Shi

Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization
Long Zhao*, Yuxiao Wang, Jiaping Zhao, Liangzhe Yuan, Jennifer J. Sun, Florian Schroff, Hartwig Adam, Xi Peng, Dimitris Metaxas, Ting Liu

Benchmarking Representation Learning for Natural World Image Collections
Grant Van Horn, Elijah Cole, Sara Beery, Kimberly Wilber, Serge Belongie, Oisin Mac Aodha

Scaling Local Self-Attention for Parameter Efficient Visual Backbones
Ashish Vaswani, Prajit Ramachandran, Aravind Srinivas, Niki Parmar, Blake Hechtman, Jonathon Shlens

KeypointDeformer: Unsupervised 3D Keypoint Discovery for Shape Control
Tomas Jakab*, Richard Tucker, Ameesh Makadia, Jiajun Wu, Noah Snavely, Angjoo Kanazawa

HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching
Vladimir Tankovich, Christian Häne, Yinda Zhang, Adarsh Kowdle, Sean Fanello, Sofien Bouaziz

POSEFusion: Pose-Guided Selective Fusion for Single-View Human Volumetric Capture
Zhe Li, Tao Yu, Zerong Zheng, Kaiwen Guo, Yebin Liu

Workshops (only Google affiliations are noted)

Media Forensics
Organizers: Christoph Bregler

Safe Artificial Intelligence for Automated Driving
Invited Speakers: Been Kim

VizWiz Grand Challenge
Organizers: Meredith Morris

3D Vision and Robotics
Invited Speaker: Andy Zeng

New Trends in Image Restoration and Enhancement Workshop and Challenges on Image and Video Processing
Organizers: Ming-Hsuan Yang Program Committee: George Toderici, Ming-Hsuan Yang

2nd Workshop on Extreme Vision Modeling
Invited Speakers: Quoc Le, Chen Sun

First International Workshop on Affective Understanding in Video
Organizers: Gautam Prasad, Ting Liu

Adversarial Machine Learning in Real-World Computer Vision Systems and Online Challenges
Program Committee: Nicholas Carlini, Nicolas Papernot

Ethical Considerations in Creative Applications of Computer Vision
Invited Speaker: Alex Hanna Organizers: Negar Rostamzadeh, Emily Denton, Linda Petrini

Visual Question Answering Workshop
Invited Speaker: Vittorio Ferrari

Sixth International Skin Imaging Collaboration (ISIC) Workshop on Skin Image Analysis
Invited Speakers: Sandra Avila Organizers: Yuan Liu Steering Committee: Yuan Liu, Dale Webster

The 4th Workshop and Prize Challenge: Bridging the Gap between Computational Photography and Visual Recognition (UG2+) in Conjunction with IEEE CVPR 2021
Invited Speakers: Peyman Milanfar, Chelsea Finn

The 3rd CVPR Workshop on 3D Scene Understanding for Vision, Graphics, and Robotics
Invited Speaker: Andrea Tagliasacchi

Robust Video Scene Understanding: Tracking and Video Segmentation
Organizers: Jordi Pont-Tuset, Sergi Caelles, Jack Valmadre, Alex Bewley

4th Workshop and Challenge on Learned Image Compression
Invited Speaker: Rianne van den Berg Organizers: George Toderici, Lucas Theis, Johannes Ballé, Eirikur Agustsson, Nick Johnston, Fabian Mentzer

The Third Workshop on Precognition: Seeing Through the Future
Invited Speaker: Anelia Angelova
Organizers: Utsav Prabhu Program Committee: Chen Sun, David Ross

Computational Cameras and Displays
Organizers: Tali Dekel Keynote Talks: Paul Debevec Program Committee: Ayan Chakrabarti, Tali Dekel

2nd Embodied AI Workshop
Organizing Committee: Anthony Francis Challenge Organizers: Peter Anderson, Anthony Francis, Alex Ku, Alexander Toshev Scientific Advisory Board: Alexander Toshev

Responsible Computer Vision
Program Committee: Caroline Pantofaru, Utsav Prabhu, Susanna Ricco, Negar Rostamzadeh, Candice Schumann

Dynamic Neural Networks Meets Computer Vision
Invited Speaker: Azalia Mirhoseini

Interactive Workshop on Bridging the Gap between Subjective and Computational Measurements of Machine Creativity
Invited Speaker: David Bau

GAZE 2021: The 3rd International Workshop on Gaze Estimation and Prediction in the Wild
Organizer: Thabo Beeler Program Committee: Thabo Beeler

Sight and Sound
Organizers: William Freeman

Future of Computer Vision Datasets
Invited Speaker: Emily Denton, Caroline Pantofaru

Open World Vision
Invited Speakers: Rahul Sukthankar

The 3rd Workshop on Learning from Unlabeled Videos
Organizers: Anelia Angelova, Honglak Lee Program Committee: AJ Piergiovanni

4th International Workshop on Visual Odometry and Computer Vision Applications Based on Location Clues — With a Focus on Mobile Platform Applications
Organizers: Anelia Angelova

4th Workshop on Efficient Deep Learning for Computer Vision
Invited Speaker: Andrew Howard
Organizers: Pete Warden, Andrew Howard

Second International Workshop on Large Scale Holistic Video Understanding
Invited Speaker: Cordelia Schmid Program Committee: AJ Piergiovanni Organizers: David Ross

Neural Architecture Search 1st Lightweight NAS Challenge and Moving Beyond
Invited Speakers: Sara Sabour

The Second Workshop on Fair, Data-Efficient, and Trusted Computer Vision
Invited Speakers: Gaurav Aggarwal

The 17th Embedded Vision Workshop
General Chair: Anelia Angelova

8th Workshop on Fine-Grained Visual Categorization
Organizers: Christine Kaeser-Chen, Kimberly Wilber

AI for Content Creation
Invited Speaker: Tali Dekel, Jon Barron, Emily Denton Organizers: Deqing Sun

Frontiers of Monocular 3D Perception
Invited Speakers: Anelia Angelova, Cordelia Schmid, Noah Snavely

Beyond Fairness: Towards a Just, Equitable, and Accountable Computer Vision
Organizers: Emily Denton

The 1st Workshop on Future Video Conferencing
Invited Speakers: Chuo-Ling Chang, Sergi Caelles

Tutorials (only Google affiliations are noted)

Tutorial on Fairness Accountability Transparency and Ethics in Computer Vision
Organizer: Emily Denton

Data-Efficient Learning in An Imperfect World
Organizers: Boqing Gong, Ting Chen

Semantic Segmentation of Point Clouds: a Deep Learning Framework for Cultural Heritage
Invited Speaker: Manzil Zaheer

From VQA to VLN: Recent Advances in Vision-and-Language Research
Organizer: Peter Anderson * Indicates work done while at Google

Source: Google AI Blog

Google at ICLR 2021

Posted by Jaqui Herman, Research Specialist and Tim Herrmann, Program Manager

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

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

Posted by Jaqui Herman, Research Specialist and Tim Herrmann, Program Manager

Officers and Board Members
Includes: Hugo Larochelle, Tara Sainath

Organizing Committee
Includes: Sanmi Koyejo, Chelsea Finn

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

“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

Posted by Jaqui Herman and Cat Armato, Program Managers

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

Posted by Cat Armato and Emily Knapp, Program Managers

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

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Tag Archives: conferences

Google at ICLR 2023

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Outstanding Paper awards

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Papers

Source: Google AI Blog

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