The tenth Worldwide Convention on Studying Representations (ICLR 2022) kicks off this week, bringing collectively researchers, entrepreneurs, engineers and college students alike to debate and discover the quickly advancing discipline of deep studying. Fully digital this 12 months, ICLR 2022 provides convention and workshop tracks that current a few of the newest analysis in deep studying and its purposes to areas starting from laptop imaginative and prescient, speech recognition and textual content understanding to robotics, computational biology, and extra.
As a Platinum Sponsor of ICLR 2022 and Champion DEI Motion Fund contributor, Google can have a strong presence with almost 100 accepted publications and intensive participation on organizing committees and in workshops. In case you have registered for ICLR 2022, we hope you’ll watch our talks and study concerning the work performed at Google to handle advanced issues that have an effect on billions of individuals. Right here you possibly can study extra concerning the analysis we shall be presenting in addition to our normal involvement at ICLR 2022 (these with Google affiliations in daring).
Senior Space Chairs:
Consists of: Been Kim, Dale Schuurmans, Sergey Levine
Space Chairs:
Consists of: Adam White, Aditya Menon, Aleksandra Faust, Amin Karbasi, Amir Globerson, Andrew Dai, Balaji Lakshminarayanan, Behnam Neyshabur, Ben Poole, Bhuwan Dhingra, Bo Dai, Boqing Gong, Cristian Sminchisescu, David Ha, David Woodruff, Denny Zhou, Dipanjan Das, Dumitru Erhan, Dustin Tran, Emma Strubell, Eunsol Choi, George Dahl, George Tucker, Hanie Sedghi, Heinrich Jiang, Hossein Mobahi, Hugo Larochelle, Izhak Shafran, Jasper Snoek, Jean-Philippe Vert, Jeffrey Pennington, Justin Gilmer, Karol Hausman, Kevin Swersky, Krzysztof Choromanski, Mathieu Blondel, Matt Kusner, Michael Ryoo, Ming-Hsuan Yang, Minmin Chen, Mirella Lapata, Mohammad Ghavamzadeh, Mohammad Norouzi, Naman Agarwal, Nicholas Carlini, Olivier Bachem, Piyush Rai, Prateek Jain, Quentin Berthet, Richard Nock, Rose Yu, Sewoong Oh, Silvio Lattanzi, Slav Petrov, Srinadh Bhojanapalli, Tim Salimans, Ting Chen, Tong Zhang, Vikas Sindhwani, Weiran Wang, William Cohen, Xiaoming Liu
Workflow Chairs:
Consists of: Yaguang Li
Variety Fairness & Inclusion Chairs:
Consists of: Rosanne Liu
Invited Talks
Past Interpretability: Creating a Language to Form Our Relationships with AI
Google Speaker: Been Kim
Do You See What I See? Giant-Scale Studying from Multimodal Movies
Google Speaker: Cordelia Schmid
Publications
Hyperparameter Tuning with Renyi Differential Privateness – 2022 Excellent Paper Award
Nicolas Papernot, Thomas Steinke
MIDI-DDSP: Detailed Management of Musical Efficiency by way of Hierarchical Modeling
Yusong Wu, Ethan Manilow, Yi Deng, Rigel Swavely, Kyle Kastner, Tim Cooijmans, Aaron Courville, Cheng-Zhi Anna Huang, Jesse Engel
The Data Geometry of Unsupervised Reinforcement Studying
Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine
Studying Strides in Convolutional Neural Networks – 2022 Excellent Paper Award
Rachid Riad*, Olivier Teboul, David Grangier, Neil Zeghidour
Poisoning and Backdooring Contrastive Studying
Nicholas Carlini, Andreas Terzis
Coordination Amongst Neural Modules By means of a Shared International Workspace
Anirudh Goyal, Aniket Didolkar, Alex Lamb, Kartikeya Badola, Nan Rosemary Ke, Nasim Rahaman, Jonathan Binas, Charles Blundell, Michael Mozer, Yoshua Bengio
Tremendous-Tuned Language Fashions Are Zero-Shot Learners (see the weblog publish)
Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, Quoc V. Le
Giant Language Fashions Can Be Robust Differentially Personal Learners
Xuechen Li, Florian Tramèr, Percy Liang, Tatsunori Hashimoto
Progressive Distillation for Quick Sampling of Diffusion Fashions
Tim Salimans, Jonathan Ho
Exploring the Limits of Giant Scale Pre-training
Samira Abnar, Mostafa Dehghani, Behnam Neyshabur, Hanie Sedghi
Scarf: Self-Supervised Contrastive Studying Utilizing Random Function Corruption
Dara Bahri, Heinrich Jiang, Yi Tay, Donald Metzler
Scalable Sampling for Nonsymmetric Determinantal Level Processes
Insu Han, Mike Gartrell, Jennifer Gillenwater, Elvis Dohmatob, Amin Karbasi
When Imaginative and prescient Transformers Outperform ResNets with out Pre-training or Robust Knowledge Augmentations
Xiangning Chen, Cho-Jui Hsieh, Boqing Gong
ViTGAN: Coaching GANs with Imaginative and prescient Transformers
Kwonjoon Lee, Huiwen Chang, Lu Jiang, Han Zhang, Zhuowen Tu, Ce Liu
Generalized Resolution Transformer for Offline Hindsight Data Matching
Hiroki Furuta, Yutaka Matsuo, Shixiang Shane Gu
The MultiBERTs: BERT Reproductions for Robustness Evaluation
Thibault Sellam, Steve Yadlowsky, Ian Tenney, Jason Wei, Naomi Saphra, Alexander D’Amour, Tal Linzen, Jasmijn Bastings, Iulia Turc, Jacob Eisenstein, Dipanjan Das, Ellie Pavlick
Scaling Legal guidelines for Neural Machine Translation
Behrooz Ghorbani, Orhan Firat, Markus Freitag, Ankur Bapna, Maxim Krikun, Xavier Garcia, Ciprian Chelba, Colin Cherry
Interpretable Unsupervised Variety Denoising and Artefact Removing
Mangal Prakash, Mauricio Delbracio, Peyman Milanfar, Florian Jug
Understanding Latent Correlation-Primarily based Multiview Studying and Self-Supervision: An Identifiability Perspective
Qi Lyu, Xiao Fu, Weiran Wang, Songtao Lu
Memorizing Transformers
Yuhuai Wu, Markus N. Rabe, DeLesley Hutchins, Christian Szegedy
Churn Discount by way of Distillation
Heinrich Jiang, Harikrishna Narasimhan, Dara Bahri, Andrew Cotter, Afshin Rostamizadeh
DR3: Worth-Primarily based Deep Reinforcement Studying Requires Express Regularization
Aviral Kumar, Rishabh Agarwal, Tengyu Ma, Aaron Courville, George Tucker, Sergey Levine
Path Auxiliary Proposal for MCMC in Discrete House
Haoran Solar, Hanjun Dai, Wei Xia, Arun Ramamurthy
On the Relation Between Statistical Studying and Perceptual Distances
Alexander Hepburn, Valero Laparra, Raul Santos-Rodriguez, Johannes Ballé, Jesús Malo
Chance Earlier than Utility: Studying And Utilizing Hierarchical Affordances
Robby Costales, Shariq Iqbal, Fei Sha
MT3: Multi-Activity Multitrack Music Transcription
Josh Gardner*, Ian Simon, Ethan Manilow*, Curtis Hawthorne, Jesse Engel
Bayesian Neural Community Priors Revisited
Vincent Fortuin, Adrià Garriga-Alonso, Sebastian W. Ober, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison
GradMax: Rising Neural Networks utilizing Gradient Data
Utku Evci, Bart van Merrienboer, Thomas Unterthiner, Fabian Pedregosa, Max Vladymyrov
Scene Transformer: A Unified Structure for Predicting Future Trajectories of A number of Brokers
Jiquan Ngiam, Benjamin Caine, Vijay Vasudevan, Zhengdong Zhang, Hao-Tien Lewis Chiang, Jeffrey Ling, Rebecca Roelofs, Alex Bewley, Chenxi Liu, Ashish Venugopal, David Weiss, Ben Sapp, Zhifeng Chen, Jonathon Shlens
The Position of Pretrained Representations for the OOD Generalization of RL Brokers
Frederik Träuble, Andrea Dittadi, Manuel Wüthrich, Felix Widmaier, Peter Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer
Autoregressive Diffusion Fashions
Emiel Hoogeboom, Alexey A. Gritsenko, Jasmijn Bastings, Ben Poole, Rianne van den Berg, Tim Salimans
The Position of Permutation Invariance in Linear Mode Connectivity of Neural Networks
Rahim Entezari, Hanie Seghi, Olga Saukh, Behnam Neyshabur
DISSECT: Disentangled Simultaneous Explanations by way of Idea Traversals
Asma Ghandeharioun, Been Kim, Chun-Liang Li, Brendan Jou, Brian Eoff, Rosalind W. Picard
Anisotropic Random Function Regression in Excessive Dimensions
Gabriel C. Mel, Jeffrey Pennington
Open-Vocabulary Object Detection by way of Imaginative and prescient and Language Information Distillation
Xiuye Gu, Tsung-Yi Lin*, Weicheng Kuo, Yin Cui
MCMC Ought to Combine: Studying Vitality-Primarily based Mannequin with Circulate-Primarily based Spine
Erik Nijkamp*, Ruiqi Gao, Pavel Sountsov, Srinivas Vasudevan, Bo Pang, Music-Chun Zhu, Ying Nian Wu
Impact of Scale on Catastrophic Forgetting in Neural Networks
Vinay Ramasesh, Aitor Lewkowycz, Ethan Dyer
Incremental False Unfavorable Detection for Contrastive Studying
Tsai-Shien Chen, Wei-Chih Hung, Hung-Yu Tseng, Shao-Yi Chien, Ming-Hsuan Yang
In the direction of Evaluating the Robustness of Neural Networks Realized by Transduction
Jiefeng Chen, Xi Wu, Yang Guo, Yingyu Liang, Somesh Jha
What Do We Imply by Generalization in Federated Studying?
Honglin Yuan*, Warren Morningstar, Lin Ning, Karan Singhal
ViDT: An Environment friendly and Efficient Totally Transformer-Primarily based Object Detector
Hwanjun Music, Deqing Solar, Sanghyuk Chun, Varun Jampani, Dongyoon Han, Byeongho Heo, Wonjae Kim, Ming-Hsuan Yang
Measuring CLEVRness: Black-Field Testing of Visible Reasoning Fashions
Spyridon Mouselinos, Henryk Michalewski, Mateusz Malinowski
Knowledge of Committees: An Neglected Strategy To Sooner and Extra Correct Fashions (see the weblog publish)
Xiaofang Wang, Dan Kondratyuk, Eric Christiansen, Kris M. Kitani, Yair Alon (prev. Movshovitz-Attias), Elad Eban
Leveraging Unlabeled Knowledge to Predict Out-of-Distribution Efficiency
Saurabh Garg*, Sivaraman Balakrishnan, Zachary C. Lipton, Behnam Neyshabur, Hanie Sedghi
Knowledge-Pushed Offline Optimization for Architecting {Hardware} Accelerators (see the weblog publish)
Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi, Kevin Swersky, Sergey Levine
Diurnal or Nocturnal? Federated Studying of Multi-branch Networks from Periodically Shifting Distributions
Chen Zhu*, Zheng Xu, Mingqing Chen, Jakub Konecny, Andrew Exhausting, Tom Goldstein
Coverage Gradients Incorporating the Future
David Venuto, Elaine Lau, Doina Precup, Ofir Nachum
Discrete Representations Strengthen Imaginative and prescient Transformer Robustness
Chengzhi Mao*, Lu Jiang, Mostafa Dehghani, Carl Vondrick, Rahul Sukthankar, Irfan Essa
SimVLM: Easy Visible Language Mannequin Pretraining with Weak Supervision (see the weblog publish)
Zirui Wang, Jiahui Yu, Adams Wei Yu, Zihang Dai, Yulia Tsvetkov, Yuan Cao
Neural Stochastic Twin Dynamic Programming
Hanjun Dai, Yuan Xue, Zia Syed, Dale Schuurmans, Bo Dai
PolyLoss: A Polynomial Growth Perspective of Classification Loss Capabilities
Zhaoqi Leng, Mingxing Tan, Chenxi Liu, Ekin Dogus Cubuk, Xiaojie Shi, Shuyang Cheng, Dragomir Anguelov
Data Prioritization By means of Empowerment in Visible Mannequin-Primarily based RL
Homanga Bharadhwaj*, Mohammad Babaeizadeh, Dumitru Erhan, Sergey Levine
Worth Operate Areas: Ability-Centric State Abstractions for Lengthy-Horizon Reasoning
Dhruv Shah, Peng Xu, Yao Lu, Ted Xiao, Alexander Toshev, Sergey Levine, Brian Ichter
Understanding and Leveraging Overparameterization in Recursive Worth Estimation
Chenjun Xiao, Bo Dai, Jincheng Mei, Oscar Ramirez, Ramki Gummadi, Chris Harris, Dale Schuurmans
The Effectivity Misnomer
Mostafa Dehghani, Anurag Arnab, Lucas Beyer, Ashish Vaswani, Yi Tay
On the Position of Inhabitants Heterogeneity in Emergent Communication
Mathieu Rita, Florian Strub, Jean-Bastien Grill, Olivier Pietquin, Emmanuel Dupoux
No One Illustration to Rule Them All: Overlapping Options of Coaching Strategies
Raphael Gontijo-Lopes, Yann Dauphin, Ekin D. Cubuk
Knowledge Poisoning Received’t Save You From Facial Recognition
Evani Radiya-Dixit, Sanghyun Hong, Nicholas Carlini, Florian Tramèr
AdaMatch: A Unified Strategy to Semi-Supervised Studying and Area Adaptation
David Berthelot, Rebecca Roelofs, Kihyuk Sohn, Nicholas Carlini, Alex Kurakin
Most Entropy RL (Provably) Solves Some Strong RL Issues
Benjamin Eysenbach, Sergey Levine
Auto-scaling Imaginative and prescient Transformers With out Coaching
Wuyang Chen, Wei Huang, Xianzhi Du, Xiaodan Music, Zhangyang Wang, Denny Zhou
Optimizing Few-Step Diffusion Samplers by Gradient Descent
Daniel Watson, William Chan, Jonathan Ho, Mohammad Norouzi
ExT5: In the direction of Excessive Multi-Activity Scaling for Switch Studying
Vamsi Aribandi, Yi Tay, Tal Schuster, Jinfeng Rao, Huaixiu Steven Zheng, Sanket Vaibhav Mehta, Honglei Zhuang, Vinh Q. Tran, Dara Bahri, Jianmo Ni, Jai Gupta, Kai Hui, Sebastian Ruder, Donald Metzler
Fortuitous Forgetting in Connectionist Networks
Hattie Zhou, Ankit Vani, Hugo Larochelle, Aaron Courville
Evading Adversarial Instance Detection Defenses with Orthogonal Projected Gradient Descent
Oliver Bryniarski, Nabeel Hingun, Pedro Pachuca, Vincent Wang, Nicholas Carlini
Benchmarking the Spectrum of Agent Capabilities
Danijar Hafner
Charformer: Quick Character Transformers by way of Gradient-Primarily based Subword Tokenization
Yi Tay, Vinh Q. Tran, Sebastian Ruder, Jai Gupta, Hyung Received Chung, Dara Bahri, Zhen Qin, Simon Baumgartner, Cong Yu, Donald Metzler
Point out Reminiscence: Incorporating Textual Information into Transformers By means of Entity Point out Consideration
Michiel de Jong, Yury Zemlyanskiy, Nicholas FitzGerald, Fei Sha, William Cohen
Eigencurve: Optimum Studying Price Schedule for SGD on Quadratic Aims with Skewed Hessian Spectrums
Rui Pan, Haishan Ye, Tong Zhang
Scale Effectively: Insights from Pre-training and Tremendous-Tuning Transformers
Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Received Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler
Omni-Scale CNNs: A Easy and Efficient Kernel Dimension Configuration for Time Collection Classification
Wensi Tang, Guodong Lengthy, Lu Liu,Tianyi Zhou, Michael Blumenstein, Jing Jiang
Embedded-Mannequin Flows: Combining the Inductive Biases of Mannequin-Free Deep Studying and Express Probabilistic Modeling
Gianluigi Silvestri, Emily Fertig, Dave Moore, Luca Ambrogioni
Publish Hoc Explanations Could also be Ineffective for Detecting Unknown Spurious Correlation
Julius Adebayo, Michael Muelly, Hal Abelson, Been Kim
Axiomatic Explanations for Visible Search, Retrieval, and Similarity Studying
Mark Hamilton, Scott Lundberg, Stephanie Fu, Lei Zhang, William T. Freeman
Pix2seq: A Language Modeling Framework for Object Detection (see the weblog publish)
Ting Chen, Saurabh Saxena, Lala Li, David J. Fleet, Geoffrey Hinton
Mirror Descent Coverage Optimization
Manan Tomar, Lior Shani, Yonathan Efroni, Mohammad Ghavamzadeh
CodeTrek: Versatile Modeling of Code Utilizing an Extensible Relational Illustration
Pardis Pashakhanloo, Aaditya Naik, Yuepeng Wang, Hanjun Dai, Petros Maniatis, Mayur Naik
Conditional Object-Centric Studying From Video
Thomas Kipf, Gamaleldin F. Elsayed, Aravindh Mahendran, Austin Stone, Sara Sabour, Georg Heigold, Rico Jonschkowski, Alexey Dosovitskiy, Klaus Greff
A Loss Curvature Perspective on Coaching Instabilities of Deep Studying Fashions
Justin Gilmer, Behrooz Ghorbani, Ankush Garg, Sneha Kudugunta, Behnam Neyshabur, David Cardoze, George E. Dahl, Zack Nado, Orhan Firat
Autonomous Reinforcement Studying: Formalism and Benchmarking
Archit Sharma, Kelvin Xu, Nikhil Sardana, Abhishek Gupta, Karol Hausman, Sergey Levine, Chelsea Finn
TRAIL: Close to-Optimum Imitation Studying with Suboptimal Knowledge
Mengjiao Yang, Sergey Levine, Ofir Nachum
Minimax Optimization With Clean Algorithmic Adversaries
Tanner Fiez, Lillian J. Ratliff, Chi Jin, Praneeth Netrapalli
Unsupervised Semantic Segmentation by Distilling Function Correspondences
Mark Hamilton, Zhoutong Zhang, Bharath Hariharan, Noah Snavely, William T. Freeman
InfinityGAN: In the direction of Infinite-Pixel Picture Synthesis
Chieh Hubert Lin, Hsin-Ying Lee, Yen-Chi Cheng, Sergey Tulyakov, Ming-Hsuan Yang
Shuffle Personal Stochastic Convex Optimization
Albert Cheu, Matthew Joseph, Jieming Mao, Binghui Peng
Hybrid Random Options
Krzysztof Choromanski, Haoxian Chen, Han Lin, Yuanzhe Ma, Arijit Sehanobish, Deepali Jain, Michael S Ryoo, Jake Varley, Andy Zeng, Valerii Likhosherstov, Dmitry Kalashnikov, Vikas Sindhwani, Adrian Weller
Vector-Quantized Picture Modeling With Improved VQGAN
Jiahui Yu, Xin Li, Jing Yu Koh, Han Zhang, Ruoming Pang, James Qin, Alexander Ku, Yuanzhong Xu, Jason Baldridge, Yonghui Wu
On the Advantages of Most Chance Estimation for Regression and Forecasting
Pranjal Awasthi, Abhimanyu Das, Rajat Sen, Ananda Theertha Suresh
Surrogate Hole Minimization Improves Sharpness-Conscious Coaching
Juntang Zhuang*, Boqing Gong, Liangzhe Yuan, Yin Cui, Hartwig Adam, Nicha C. Dvornek, Sekhar Tatikonda, James S. Duncan, Ting Liu
On-line Goal Q-learning With Reverse Expertise Replay: Effectively Discovering the Optimum Coverage for Linear MDPs
Naman Agarwal, Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli, Syomantak Chaudhuri
CrossBeam: Studying to Search in Backside-Up Program Synthesis
Kensen Shi, Hanjun Dai, Kevin Ellis, Charles Sutton
Workshops
Workshop on the Parts of Reasoning: Objects, Construction, and Causality (OSC)
Organizers embody: Klaus Greff, Thomas Kipf
Workshop on Agent Studying in Open-Endedness
Organizers embody: Krishna Srinivasan
Audio system embody: Natasha Jaques, Danijar Hafner
Wiki-M3L: Wikipedia and Multi-modal & Multi-lingual Analysis
Organizers embody: Klaus Greff, Thomas Kipf
Audio system embody: Jason Baldridge, Tom Duerig
Setting Up ML Analysis Requirements to Speed up Progress
Organizers embody: Rishabh Agarwal
Audio system and Panelists embody: Katherine Heller, Sara Hooker, Corinna Cortes
From Cells to Societies: Collective Studying Throughout Scales
Organizers embody: Mark Sandler, Max Vladymyrov
Audio system embody: Blaise Aguera y Arcas, Alexander Mordvintsev, Michael Mozer
Emergent Communication: New Frontiers
Audio system embody: Natasha Jaques
Deep Studying for Code
Organizers embody: Jonathan Herzig
GroundedML: Anchoring Machine Studying in Classical Algorithmic Concept
Audio system embody: Gintare Karolina Dziugaite
Generalizable Coverage Studying within the Bodily World
Audio system and Panelists embody: Mrinal Kalakrishnan
CoSubmitting Summer time (CSS) Workshop
Organizers embody: Rosanne Liu
*Work performed whereas at Google. ↩
