DataBloom

Posted by Michael Ryoo, Research Scientist, Robotics at Google and Anurag Arnab, Research Scientist, Google Research

Transformer models consistently obtain state-of-the-art results in computer vision tasks, including object detection and video classification. In contrast to standard convolutional approaches that process images pixel-by-pixel, the Vision Transformers (ViT) treat an image as a sequence of patch tokens (i.e., a smaller part, or “patch”, of an image made up of multiple pixels). This means that at every layer, a ViT model recombines and processes patch tokens based on relations between each pair of tokens, using multi-head self-attention. In doing so, ViT models have the capability to construct a global representation of the entire image.

At the input-level, the tokens are formed by uniformly splitting the image into multiple segments, e.g., splitting an image that is 512 by 512 pixels into patches that are 16 by 16 pixels. At the intermediate levels, the outputs from the previous layer become the tokens for the next layer. In the case of videos, video ‘tubelets’ such as 16x16x2 video segments (16×16 images over 2 frames) become tokens. The quality and quantity of the visual tokens decide the overall quality of the Vision Transformer.

The main challenge in many Vision Transformer architectures is that they often require too many tokens to obtain reasonable results. Even with 16×16 patch tokenization, for instance, a single 512×512 image corresponds to 1024 tokens. For videos with multiple frames, that results in tens of thousands of tokens needing to be processed at every layer. Considering that the Transformer computation increases quadratically with the number of tokens, this can often make Transformers intractable for larger images and longer videos. This leads to the question: is it really necessary to process that many tokens at every layer?

In “TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?”, an earlier version of which is presented at NeurIPS 2021, we show that adaptively generating a smaller number of tokens, rather than always relying on tokens formed by uniform splitting, enables Vision Transformers to run much faster and perform better. TokenLearner is a learnable module that takes an image-like tensor (i.e., input) and generates a small set of tokens. This module could be placed at various different locations within the model of interest, significantly reducing the number of tokens to be handled in all subsequent layers. The experiments demonstrate that having TokenLearner saves memory and computation by half or more without damaging classification performance, and because of its ability to adapt to inputs, it even increases the accuracy.

The TokenLearner
We implement TokenLearner using a straightforward spatial attention approach. In order to generate each learned token, we compute a spatial attention map highlighting regions-of-importance (using convolutional layers or MLPs). Such a spatial attention map is then applied to the input to weight each region differently (and discard unnecessary regions), and the result is spatially pooled to generate the final learned tokens. This is repeated multiple times in parallel, resulting in a few (~10) tokens out of the original input. This can also be viewed as performing a soft-selection of the pixels based on the weight values, followed by global average pooling. Note that the functions to compute the attention maps are governed by different sets of learnable parameters, and are trained in an end-to-end fashion. This allows the attention functions to be optimized in capturing different spatial information in the input. The figure below illustrates the process.

The TokenLearner module learns to generate a spatial attention map for each output token, and uses it to abstract the input to tokenize. In practice, multiple spatial attention functions are learned, are applied to the input, and generate different token vectors in parallel.

As a result, instead of processing fixed, uniformly tokenized inputs, TokenLearner enables models to process a smaller number of tokens that are relevant to the specific recognition task. That is, (1) we enable adaptive tokenization so that the tokens can be dynamically selected conditioned on the input, and (2) this effectively reduces the total number of tokens, greatly reducing the computation performed by the network. These dynamically and adaptively generated tokens can be used in standard transformer architectures such as ViT for images and ViViT for videos.

Where to Place TokenLearner
After building the TokenLearner module, we had to determine where to place it. We first tried placing it at different locations within the standard ViT architecture with 224×224 images. The number of tokens TokenLearner generated was 8 and 16, much less than 196 or 576 tokens the standard ViTs use. The below figure shows ImageNet few-shot classification accuracies and FLOPS of the models with TokenLearner inserted at various relative locations within ViT B/16, which is the base model with 12 attention layers operating on 16×16 patch tokens.

Top: ImageNet 5-shot transfer accuracy with JFT 300M pre-training, with respect to the relative TokenLearner locations within ViT B/16. Location 0 means TokenLearner is placed before any Transformer layer. Base is the original ViT B/16. Bottom: Computation, measured in terms of billions of floating point operations (GFLOPS), per relative TokenLearner location.

We found that inserting TokenLearner after the initial quarter of the network (at 1/4) achieves almost identical accuracies as the baseline, while reducing the computation to less than a third of the baseline. In addition, placing TokenLearner at the later layer (after 3/4 of the network) achieves even better performance compared to not using TokenLearner while performing faster, thanks to its adaptiveness. Due to the large difference between the number of tokens before and after TokenLearner (e.g., 196 before and 8 after), the relative computation of the transformers after the TokenLearner module becomes almost negligible.

Comparing Against ViTs
We compared the standard ViT models with TokenLearner against those without it while following the same setting on ImageNet few-shot transfer. TokenLearner was placed in the middle of each ViT model at various locations such as at 1/2 and at 3/4. The below figure shows the performance/computation trade-off of the models with and without TokenLearner.

Performance of various versions of ViT models with and without TokenLearner, on ImageNet classification. The models were pre-trained with JFT 300M. The closer a model is to the top-left of each graph the better, meaning that it runs faster and performs better. Observe how TokenLearner models perform better than ViT in terms of both accuracy and computation.

We also inserted TokenLearner within larger ViT models, and compared them against the giant ViT G/14 model. Here, we applied TokenLearner to ViT L/10 and L/8, which are the ViT models with 24 attention layers taking 10×10 (or 8×8) patches as initial tokens. The below figure shows that despite using many fewer parameters and less computation, TokenLearner performs comparably to the giant G/14 model with 48 layers.

Left: Classification accuracy of large-scale TokenLearner models compared to ViT G/14 on ImageNet datasets. Right: Comparison of the number of parameters and FLOPS.

High-Performing Video Models
Video understanding is one of the key challenges in computer vision, so we evaluated TokenLearner on multiple video classification datasets. This was done by adding TokenLearner into Video Vision Transformers (ViViT), which can be thought of as a spatio-temporal version of ViT. TokenLearner learned 8 (or 16) tokens per timestep.

When combined with ViViT, TokenLearner obtains state-of-the-art (SOTA) performance on multiple popular video benchmarks, including Kinetics-400, Kinetics-600, Charades, and AViD, outperforming the previous Transformer models on Kinetics-400 and Kinetics-600 as well as previous CNN models on Charades and AViD.

Models with TokenLearner outperform state-of-the-art on popular video benchmarks (captured from Nov. 2021). Left: popular video classification tasks. Right: comparison to ViViT models.

Visualization of the spatial attention maps in TokenLearner, over time. As the person is moving in the scene, TokenLearner pays attention to different spatial locations to tokenize.

Conclusion
While Vision Transformers serve as powerful models for computer vision, a large number of tokens and their associated computation amount have been a bottleneck for their application to larger images and longer videos. In this project, we illustrate that retaining such a large number of tokens and fully processing them over the entire set of layers is not necessary. Further, we demonstrate that by learning a module that extracts tokens adaptively based on the input image allows attaining even better performance while saving compute. The proposed TokenLearner was particularly effective in video representation learning tasks, which we confirmed with multiple public datasets. A preprint of our work as well as code are publicly available.

Acknowledgement
We thank our co-authors: AJ Piergiovanni, Mostafa Dehghani, and Anelia Angelova. We also thank the Robotics at Google team members for the motivating discussions.

Posted by Jaqui Herman and Cat Armato, Program Managers

This week marks the beginning of the 35^th annual Conference on Neural Information Processing Systems (NeurIPS 2021), the biggest machine learning conference of the year. NeurIPS 2021 will be held virtually and includes invited talks, demonstrations and presentations of some of the latest in machine learning research. This year, NeurIPS also announced a new Datasets and Benchmarks track, which will include publications, talks, posters, and discussions related to this research area.

Google will have a strong presence with more than 170 accepted papers, additionally contributing to and learning from the broader academic research community via talks, posters, workshops, and tutorials. You can learn more about our work being presented in the list below (Google affiliations highlighted in bold).

Organizing Committee

Communications Co-Chair: Emily Denton
Program Co-Chair: Yann Dauphin
Workshop Co-Chair: Sanmi Koyejo

Senior Area Chairs: Alekh Agarwal, Amir Globerson, Been Kim, Charles Sutton, Claudio Gentile, Corinna Cortes, Dale Schuurmans, David Duvenaud, Elad Hazan, Hugo Larochelle, Jean-Philippe Vert, Kevin Murphy, Marco Cuturi, Mehryar Mohri, Mohammad Ghavamzadeh, Samory Kpotufe, Sanjiv Kumar, Satyen Kale, Sergey Levine, Tara N. Sainath, Yishay Mansour

Area Chairs: Abhishek Kumar, Abhradeep Guha Thakurta, Alex Kulesza, Alexander A. Alemi, Alexander T. Toshev, Amin Karbasi, Amit Daniely, Ananda Theertha Suresh, Ankit Singh Rawat, Ashok Cutkosky, Badih Ghazi, Balaji Lakshminarayanan, Ben Poole, Bo Dai, Boqing Gong, Chelsea Finn, Chiyuan Zhang, Christian Szegedy, Cordelia Schmid, Craig Boutilier, Cyrus Rashtchian, D. Sculley, Daniel Keysers, David Ha, Denny Zhou, Dilip Krishnan, Dumitru Erhan, Dustin Tran, Ekin Dogus Cubuk, Fabian Pedregosa, George Tucker, Hanie Sedghi, Hanjun Dai, Heinrich Jiang, Hossein Mobahi, Izhak Shafran, Jaehoon Lee, Jascha Sohl-Dickstein, Jasper Snoek, Jeffrey Pennington, Jelani Nelson, Jieming Mao, Justin Gilmer, Karol Hausman, Karthik Sridharan, Kevin Swersky, Maithra Raghu, Mario Lucic, Mathieu Blondel, Matt Kusner, Matthew Johnson, Matthieu Geist, Ming-Hsuan Yang, Mohammad Mahdian, Mohammad Norouzi, Nal Kalchbrenner, Naman Agarwal, Nicholas Carlini, Nicolas Papernot, Olivier Bachem, Olivier Pietquin, Paul Duetting, Praneeth Netrapalli, Pranjal Awasthi, Prateek Jain, Quentin Berthet, Renato Paes Leme, Richard Nock, Rif A. Saurous, Rose Yu, Roy Frostig, Samuel Stern Schoenholz, Sashank J. Reddi, Sercan O. Arik, Sergei Vassilvitskii, Sergey Ioffe, Shay Moran, Silvio Lattanzi, Simon Kornblith, Srinadh Bhojanapalli, Thang Luong, Thomas Steinke, Tim Salimans, Tomas Pfister, Tomer Koren, Uri Stemmer, Vahab Mirrokni, Vikas Sindhwani, Vincent Dumoulin, Virginia Smith, Vladimir Braverman, W. Ronny Huang, Wen Sun, Yang Li, Yasin Abbasi-Yadkori, Yinlam Chow,Yujia Li, Yunhe Wang, Zoltán Szabó

NeurIPS Foundation Board 2021: Michael Mozer, Corinna Cortes, Hugo Larochelle, John C. Platt, Fernando Pereira

Test of Time Award

Online Learning for Latent Dirichlet Allocation
Matthew D. Hoffman^†, David M. Blei, Francis Bach

Publications

Deep Reinforcement Learning at the Edge of the Statistical Precipice (see blog post)
Outstanding Paper Award Recipient
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron Courville, Marc G. Bellemare

A Separation Result Between Data-Oblivious and Data-Aware Poisoning Attacks
Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Abhradeep Guha Thakurta

Adversarial Robustness of Streaming Algorithms Through Importance Sampling
Vladimir Braverman, Avinatan Hassidim, Yossi Matias, Mariano Schain, Sandeep Silwal, Samson Zhou

Aligning Silhouette Topology for Self-Adaptive 3D Human Pose Recovery
Mugallodi Rakesh, Jogendra Nath Kundu, Varun Jampani, R. Venkatesh Babu

Attention Bottlenecks for Multimodal Fusion
Arsha Nagrani, Shan Yang, Anurag Arnab, Aren Jansen, Cordelia Schmid, Chen Sun

Autonomous Reinforcement Learning via Subgoal Curricula
Archit Sharma, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn

Calibration and Consistency of Adversarial Surrogate Losses
Pranjal Awasthi, Natalie S. Frank, Anqi Mao, Mehryar Mohri, Yutao Zhong

Compressive Visual Representations
Kuang-Huei Lee, Anurag Arnab, Sergio Guadarrama, John Canny, Ian Fischer

Counterfactual Invariance to Spurious Correlations in Text Classification
Victor Veitch, Alexander D’Amour, Steve Yadlowsky, Jacob Eisenstein

Deep Learning Through the Lens of Example Difficulty
Robert J.N. Baldock, Hartmut Maennel, Behnam Neyshabur

Deep Neural Networks as Point Estimates for Deep Gaussian Processes
Vinent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande

Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning
Ligeng Zhu, Hongzhou Lin, Yao Lu, Yujun Lin, Song Han

Discrete-Valued Neural Communication
Dianbo Liu, Alex Lamb, Kenji Kawaguchi, Anirudh Goyal, Chen Sun, Michael Curtis Mozer, Yoshua Bengio

Do Vision Transformers See Like Convolutional Neural Networks?
Maithra Raghu, Thomas Unterthiner, Simon Kornblith, Chiyuan Zhang, Alexey Dosovitskiy

Dueling Bandits with Team Comparisons
Lee Cohen, Ulrike Schmidt-Kraepelin, Yishay Mansour

End-to-End Multi-Modal Video Temporal Grounding
Yi-Wen Chen, Yi-Hsuan Tsai, Ming-Hsuan Yang

Environment Generation for Zero-Shot Compositional Reinforcement Learning
Izzeddin Gur, Natasha Jaques, Yingjie Miao, Jongwook Choi, Manoj Tiwari, Honglak Lee, Aleksandra Faust

H-NeRF: Neural Radiance Fields for Rendering and Temporal Reconstruction of Humans in Motion
Hongyi Xu, Thiemo Alldieck, Cristian Sminchisescu

Improving Calibration Through the Relationship with Adversarial Robustness
Yao Qin, Xuezhl Wang, Alex Beutel, Ed Chi

Learning Generalized Gumbel-Max Causal Mechanisms
Guy Lorberbom, Daniel D. Johnson, Chris J. Maddison, Daniel Tarlow, Tamir Hazan

MICo: Improved Representations via Sampling-Based State Similarity for Markov Decision Processes
Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden, Mark Rowland

Near-Optimal Lower Bounds For Convex Optimization For All Orders of Smoothness
Ankit Garg, Robin Kothari, Praneeth Netrapalli, Suhail Sherif

Neural Circuit Synthesis from Specification Patterns
Frederik Schmitt, Christopher Hahn, Markus N. Rabe, Bernd Finkbeiner

Non-Local Latent Relation Distillation for Self-Adaptive 3D Human Pose Estimation
Jogendra Nath Kundu, Siddharth Seth, Anirudh Jamkhandi, Pradyumna YM, Varun Jampani, Anirban Chakraborty, R. Venkatesh Babu

Object-Aware Contrastive Learning for Debiased Scene Representation
Sangwoo Mo, Hyunwoo Kang, Kihyuk Soh, Chun-Liang Li, Jinwoo Shin

On Density Estimation with Diffusion Models
Diederik P. Kingma, Tim Salimans, Ben Poole, Jonathan Ho

On Margin-Based Cluster Recovery with Oracle Queries
Marco Bressan, Nicolo Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice

On Model Calibration for Long-Tailed Object Detection and Instance Segmentation
Tai-Yu Pan, Cheng Zhang, Yandong Li, Hexiang Hu, Dong Xuan, Soravit Changpinyo, Boqing Gong, Wei-Lun Chao

Parallelizing Thompson Sampling
Amin Karbasi, Vahab Mirrokni, Mohammad Shadravan

Reverse-Complement Equivariant Networks for DNA Sequences
Vincent Mallet, Jean-Philippe Vert

Revisiting ResNets: Improved Training and Scaling Strategies
Irwan Bello, William Fedus, Xianzhi Du, Ekin Dogus Cubuk, Aravind Srinivas, Tsung-Yi Lin, Jonathon Shlens, Barret Zoph

Revisiting the Calibration of Modern Neural Networks
Matthias Minderer, Josip Djolonga, Rob Romijnders, Frances Ann Hubis, Xiaohua Zhai, Neil Houlsby, Dustin Tran, Mario Lucic

Scaling Vision with Sparse Mixture of Experts
Carlos Riquelme, Joan Puigcerver, Basil Mustafa, Maxim Neumann, Rodolphe Jenatton, André Susano Pinto, Daniel Keysers, Neil Houlsby

SE(3)-Equivariant Prediction of Molecular Wavefunctions and Electronic Densities
Oliver Thorsten Unke, Mihail Bogojeski, Michael Gastegger, Mario Geiger, Tess Smidt, Klaus Robert Muller

Stateful ODE-Nets Using Basis Function Expansions
Alejandro Francisco Queiruga, N. Benjamin Erichson, Liam Hodgkinson, Michael W. Mahoney

Statistically and Computationally Efficient Linear Meta-Representation Learning
Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh

Streaming Belief Propagation for Community Detection
Yuchen Wu, Jakab Tardos, Mohammad Hossein Bateni, André Linhares, Filipe Miguel Gonçalves de Almeida, Andrea Montanari, Ashkan Norouzi-Fard

Synthetic Design: An Optimization Approach to Experimental Design with Synthetic Controls
Nick Doudchenko, Khashayar Khosravi, Jean Pouget-Abadie, Sebastien Lahaie, Miles Lubin, Vahab Mirrokni, Jann Spiess, Guido Imbens

The Difficulty of Passive Learning in Deep Reinforcement Learning
George Ostrovski, Pablo Samuel Castro, Will Dabney

The Pareto Frontier of Model Selection for General Contextual Bandits
Teodor Marinov, Julian Zimmert

VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
Hassan Akbari, Liangzhe Yuan, Rui Qian, Wei-Hong Chuang, Shih-Fu Chang, Yin Cui, Boqing Gong

Co-Adaptation of Algorithmic and Implementational Innovations in Inference-Based Deep Reinforcement Learning
Hiroki Furuta, Tadashi Kozuno, Tatsuya Matsushima, Yutaka Matsuo, Shixiang Gu

Conservative Data Sharing for Multi-Task Offline Reinforcement Learning
Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Sergey Levine, Chelsea Finn

Does Knowledge Distillation Really Work?
Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Alexander A. Alemi, Andrew Gordon Wilson

Exponential Graph is Provably Efficient for Decentralized Deep Training
Bicheng Ying, Kun Yuan, Yiming Chen, Hanbin Hu, Pan Pan, Wotao Yin

Faster Matchings via Learned Duals
Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii

Improved Transformer for High-Resolution GANs
Long Zhao, Zizhao Zhang, Ting Chen, Dimitris N. Metaxas, Han Zhang

Near-Optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems
Prateek Jain, Suhas S. Kowshik, Dheeraj Mysore Nagaraj, Praneeth Netrapalli

Nearly Horizon-Free Offline Reinforcement Learning
Tongzheng Ren, Jialian Li, Bo Dai, Simon S. Du, Sujay Sanghavi

Overparameterization Improves Robustness to Covariate Shift in High Dimensions
Nilesh Tripuraneni, Ben Adlam, Jeffrey Pennington

Pay Attention to MLPs
Hanxiao Liu, Zihang Dai, David R. So, Quoc V. Le

PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair
Zimin Chen^*, Vincent Josua Hellendoorn^*, Pascal Lamblin, Petros Maniatis, Pierre-Antoine Manzagol, Daniel Tarlow, Subhodeep Moitra

Prior-Independent Dynamic Auctions for a Value-Maximizing Buyer
Yuan Deng, Hanrui Zhang

Remember What You Want to Forget: Algorithms for Machine Unlearning
Ayush Sekhari, Jayadev Acharya, Gautam Kamath, Ananda Theertha Suresh

Reverse Engineering Learned Optimizers Reveals Known and Novel Mechanisms
Niru Maheswaranathan^*, David Sussillo^*, Luke Metz, Ruoxi Sun, Jascha Sohl-Dickstein

Revisiting 3D Object Detection From an Egocentric Perspective
Boyang Deng, Charles R. Qi, Mahyar Najibi, Thomas Funkhouser, Yin Zhou, Dragomir Anguelov

Robust Auction Design in the Auto-Bidding World
Santiago Balseiro, Yuan Deng, Jieming Mao, Vahab Mirrokni, Song Zuo

Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data
Qi Zhu, Natalia Ponomareva, Jiawei Han, Bryan Perozzi

Understanding How Encoder-Decoder Architectures Attend
Kyle Aitken, Vinay V. Ramasesh, Yuan Cao, Niru Maheswaranathan

Understanding the Effect of Stochasticity in Policy Optimization
Jincheng Mei, Bo Dai, Chenjun Xiao, Csaba Szepesvari, Dale Schuurmans

Accurately Solving Rod Dynamics with Graph Learning
Han Shao, Tassilo Kugelstadt, Torsten Hädrich, Wojtek Palubicki, Jan Bender, Sören Pirk, Dominik L. Michels

GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training
Chen Zhu, Renkun Ni, Zheng Xu, Kezhi Kong, W. Ronny Huang, Tom Goldstein

Learnability of Linear Thresholds from Label Proportions
Rishi Saket

MLP-Mixer: An All-MLP Architecture for Vision
Ilya Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, Alexey Dosovitskiy

Neural Additive Models: Interpretable Machine Learning with Neural Nets
Rishabh Agarwal, Levi Melnick, Nicholas Frosst, Xuezhou Zhang, Ben Lengerich, Rich Caruana, Geoffrey Hinton

Neural Production Systems
Anirudh Goyal, Aniket Didolkar, Nan Rosemary Ke, Charles Blundell, Philippe Beaudoin, Nicolas Heess, Michael Mozer, Yoshua Bengio

Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling
Niv Giladi, Zvika Ben-Haim, Sella Nevo, Yossi Matias, Daniel Soudry

Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects
Denys Rozumnyi, Martin R. Oswald, Vittorio Ferrari, Marc Pollefeys

What Matters for Adversarial Imitation Learning?
Manu Orsini, Anton Raichuk, Léonard Hussenot, Damien Vincent, Robert Dadashi, Sertan Girgin, Matthieu Geist, Olivier Bachem, Olivier Pietquin, Marcin Andrychowicz

A Convergence Analysis of Gradient Descent on Graph Neural Networks
Pranjal Awasthi, Abhimanyu Das, Sreenivas Gollapudi

A Geometric Analysis of Neural Collapse with Unconstrained Features
Zhihui Zhu, Tianyu Ding, Jinxin Zhou, Xiao Li, Chong You, Jeremias Sulam, Qing Qu

Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations
Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan

Controlled Text Generation as Continuous Optimization with Multiple Constraints
Sachin Kumar, Eric Malmi, Aliaksei Severyn, Yulia Tsvetkov

Coupled Gradient Estimators for Discrete Latent Variables
Zhe Dong, Andriy Mnih, George Tucker

Detecting Errors and Estimating Accuracy on Unlabeled Data with Self-Training Ensembles
Jiefeng Chen^*, Frederick Liu, Besim Avci, Xi Wu, Yingyu Liang, Somesh Jha

Neural Active Learning with Performance Guarantees
Zhilei Wang, Pranjal Awasthi, Christoph Dann, Ayush Sekhari, Claudio Gentile

Optimal Sketching for Trace Estimation
Shuli Jiang, Hai Pham, David Woodruff, Qiuyi (Richard) Zhang

Representing Long-Range Context for Graph Neural Networks with Global Attention
Zhanghao Wu, Paras Jain, Matthew A. Wright, Azalia Mirhoseini, Joseph E. Gonzalez, Ion Stoica

Scaling Up Exact Neural Network Compression by ReLU Stability
Thiago Serra, Xin Yu, Abhinav Kumar, Srikumar Ramalingam

Soft Calibration Objectives for Neural Networks
Archit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael Curtis Mozer, Rebecca Roelofs

Sub-Linear Memory: How to Make Performers SLiM
Valerii Likhosherstov, Krzysztof Choromanski, Jared Davis, Xingyou Song, Adrian Weller

A New Theoretical Framework for Fast and Accurate Online Decision-Making
Nicolò Cesa-Bianchi, Tommaso Cesari, Yishay Mansour, Vianney Perchet

Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot Meta-Learning
Nan Ding, Xi Chen, Tomer Levinboim, Sebastian Goodman, Radu Soricut

Differentially Private Multi-Armed Bandits in the Shuffle Model
Jay Tenenbaum, Haim Kaplan, Yishay Mansour, Uri Stemmer

Efficient and Local Parallel Random Walks
Michael Kapralov, Silvio Lattanzi, Navid Nouri, Jakab Tardos

Improving Anytime Prediction with Parallel Cascaded Networks and a Temporal-Difference Loss
Michael Louis Iuzzolino, Michael Curtis Mozer, Samy Bengio^*

It Has Potential: Gradient-Driven Denoisers for Convergent Solutions to Inverse Problems
Regev Cohen, Yochai Blau, Daniel Freedman, Ehud Rivlin

Learning to Combine Per-Example Solutions for Neural Program Synthesis
Disha Shrivastava, Hugo Larochelle, Daniel Tarlow

LLC: Accurate, Multi-purpose Learnt Low-Dimensional Binary Codes
Aditya Kusupati, Matthew Wallingford, Vivek Ramanujan, Raghav Somani, Jae Sung Park, Krishna Pillutla, Prateek Jain, Sham Kakade, Ali Farhadi

There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning (see blog post)

Nathan Grinsztajn, Johan Ferret, Olivier Pietquin, Philippe Preux, Matthieu Geist

A Near-Optimal Algorithm for Debiasing Trained Machine Learning Models
Ibrahim Alabdulmohsin, Mario Lucic

Adaptive Sampling for Minimax Fair Classification
Shubhanshu Shekhar, Greg Fields, Mohammad Ghavamzadeh, Tara Javidi

Asynchronous Stochastic Optimization Robust to Arbitrary Delays
Alon Cohen, Amit Daniely, Yoel Drori, Tomer Koren, Mariano Schain

Boosting with Multiple Sources
Corinna Cortes, Mehryar Mohri, Dmitry Storcheus, Ananda Theertha Suresh

Breaking the Centralized Barrier for Cross-Device Federated Learning
Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stitch, Ananda Theertha Sureshi

Canonical Capsules: Self-Supervised Capsules in Canonical Pose
Weiwei Sun, Andrea Tagliasacchi, Boyang Deng, Sara Sabour, Soroosh Yazdani, Geoffrey Hinton, Kwang Moo Yi

Contextual Recommendations and Low-Regret Cutting-Plane Algorithms
Sreenivas Gollapudi, Guru Guruganesh, Kostas Kollias, Pasi Manurangsi, Renato Paes Leme, Jon Schneider

Decision Transformer: Reinforcement Learning via Sequence Modeling
Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee|Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch

Deep Learning on a Data Diet: Finding Important Examples Early in Training
Mansheej Paul, Surya Ganguli, Gintare Karolina Dziugaite

Deep Learning with Label Differential Privacy
Badih Ghazi, Noah Golowich^*, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang

Efficient Training of Retrieval Models Using Negative Cache
Erik Lindgren, Sashank J. Reddi, Ruiqi Guo, Sanjiv Kumar

Exploring Cross-Video and Cross-Modality Signals for Weakly-Supervised Audio-Visual Video Parsing
Yan-Bo Lin, Hung-Yu Tseng, Hsin-Ying Lee, Yen-Yu Lin, Ming-Hsuan Yang

Federated Reconstruction: Partially Local Federated Learning
Karan Singhal, Hakim Sidahmed, Zachary Garrett, Shanshan Wu, Keith Rush, Sushant Prakash

Framing RNN as a Kernel Method: A Neural ODE Approach
Adeline Fermanian, Pierre Marion, Jean-Philippe Vert, Gérard Biau

Learning Semantic Representations to Verify Hardware Designs
Shobha Vasudevan, Wenjie Jiang, David Bieber, Rishabh Singh, Hamid Shojaei, C. Richard Ho, Charles Sutton

Learning with User-Level Privacy
Daniel Asher Nathan Levy^*, Ziteng Sun^*, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh

Logarithmic Regret from Sublinear Hints
Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit

Margin-Independent Online Multiclass Learning via Convex Geometry
Guru Guruganesh, Allen Liu, Jon Schneider, Joshua Ruizhi Wang

Multiclass Boosting and the Cost of Weak Learning
Nataly Brukhim, Elad Hazan, Shay Moran, Indraneel Mukherjee, Robert E. Schapire

Neural-PIL: Neural Pre-integrated Lighting for Reflectance Decomposition
Mark Boss, Varun Jampani, Raphael Braun, Ce Liu^*, Jonathan T. Barron, Hendrik Lensch

Never Go Full Batch (in Stochastic Convex Optimization)
Idan Amir, Yair Carmon, Tomer Koren, Roi Livni

On Large-Cohort Training for Federated Learning
Zachary Charles, Zachary Garrett, Zhouyuan Huo, Sergei Shmulyian, Virginia Smith

On the Sample Complexity of Privately Learning Axis-Aligned Rectangles
Menachem Sadigurschi, Uri Stemmer

Online Control of Unknown Time-Varying Dynamical Systems
Edgar Minasyan, Paula Gradu, Max Simchowitz, Elad Hazan

Online Knapsack with Frequency Predictions
Sungjin Im, Ravi Kumar,Mahshid Montazer Qaem, Manish Purohit

Optimal Rates for Random Order Online Optimization
Uri Sherman, Tomer Koren, Yishay Mansour

Oracle-Efficient Regret Minimization in Factored MDPs with Unknown Structure
Aviv Rosenberg, Yishay Mansour

Practical Large-Scale Linear Programming Using Primal-Dual Hybrid Gradient
David Applegate, Mateo Díaz^*, Oliver Hinder, Haihao Lu^*, Miles Lubin, Brendan O’Donoghue, Warren Schudy

Private and Non-Private Uniformity Testing for Ranking Data
Robert Istvan Busa-Fekete, Dimitris Fotakis, Manolis Zampetakis

Privately Learning Subspaces
Vikrant Singhal, Thomas Steinke

Provable Representation Learning for Imitation with Contrastive Fourier Features
Ofir Nachum, Mengjiao Yang

Safe Reinforcement Learning with Natural Language Constraints
Tsung-Yen Yang, Michael Hu, Yinlam Chow, Peter J. Ramadge, Karthik Narasimhan

Searching for Efficient Transformers for Language Modeling
David R. So, Wojciech Mańke, Hanxiao Liu, Zihang Dai, Noam Shazeer, Quoc V. Le

SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression
Steve Yadlowsky, Taedong Yun, Cory McLean, Alexander D’Amour

Streaming Linear System Identification with Reverse Experience Replay
Prateek Jain, Suhas S. Kowshik, Dheeraj Mysore Nagaraj, Praneeth Netrapalli

The Skellam Mechanism for Differentially Private Federated Learning
Naman Agarwal, Peter Kairouz, Ziyu Liu^*

TokenLearner: Adaptive Space-Time Tokenization for Videos
Michael S. Ryoo, AJ Piergiovanni, Anurag Arnab, Mostafa Dehghani, Anelia Angelova

Towards Best-of-All-Worlds Online Learning with Feedback Graphs
Liad Erez, Tomer Koren

Training Over-Parameterized Models with Non-decomposable Objectives
Harikrishna Narasimhan, Aditya Krishna Menon

Twice Regularized MDPs and the Equivalence Between Robustness and Regularization
Esther Derman, Matthieu Geist, Shie Mannor

Unsupervised Learning of Compositional Energy Concepts
Yilun Du, Shuang Li, Yash Sharma, Joshua B. Tenenbaum, Igor Mordatch

User-Level Differentially Private Learning via Correlated Sampling
Badih Ghazi, Ravi Kumar, Pasin Manurangsi

ViSER: Video-Specific Surface Embeddings for Articulated 3D Shape Reconstruction
Gengshan Yang, Deqing Sun, Varun Jampani, Daniel Vlasic, Forrester Cole, Ce Liu^*, Deva Ramanan

A Minimalist Approach to Offline Reinforcement Learning
Scott Fujimoto, Shixiang Gu

A Unified View of cGANs With and Without Classifiers
Si-An Chen, Chun-Liang Li, Hsuan-Tien Lin

CoAtNet: Marrying Convolution and Attention for All Data Sizes (see blog post)
Zihang Dai, Hanxiao Liu, Quoc V. Le, Mingxing Tan

Combiner: Full Attention Transformer with Sparse Computation Cost
Hongyu Ren^*, Hanjun Dai, Zihang Dai, Mengjiao Yang, Jure Leskovec, Dale Schuurmans, Bo Dai

Contrastively Disentangled Sequential Variational Autoencoder
Junwen Bai, Weiran Wang, Carla P. Gomes

Controlling Neural Networks with Rule Representations
Sungyong Seo, Sercan O. Arik, Jinsung Yoon, Xiang Zhang, Kihyuk Sohn, Tomas Pfister

Dataset Distillation with Infinitely Wide Convolutional Networks
Timothy Nguyen^*, Roman Novak, Lechao Xiao, Jaehoon Lee

Deep Synoptic Monte-Carlo Planning in Reconnaissance Blind Chess
Gregory Clark

Differentially Private Learning with Adaptive Clipping
Galen Andrew, Om Thakkar, Swaroop Ramaswamy, Hugh Brendan McMahan

Differentially Private Model Personalization
Prateek Jain, Keith Rush, Adam Smith, Shuang Song, Abhradeep Thakurta

Efficient Algorithms for Learning Depth-2 Neural Networks with General ReLU Activations
Pranjal Awasthi, Alex Tang, Aravindan Vijayaraghavan

Efficiently Identifying Task Groupings for Multi-Task Learning
Christopher Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, Chelsea Finn

Generalized Shape Metrics on Neural Representations
Alex H. Williams, Erin Kunz, Simon Kornblith, Scott Linderman

High-Probability Bounds for Non-Convex Stochastic Optimization with Heavy Tails
Ashok Cutkosky, Harsh Mehta

Identity Testing for Mallows Model
Róbert Busa-Fekete, Dimitris Fotakis, Balázs Szörényi, Manolis Zampetakis

Learnable Fourier Features for Multi-dimensional Spatial Positional Encoding
Yang Li, Si Si, Gang Li, Cho-Jui Hsieh, Samy Bengio^*

Learning to Select Exogenous Events for Marked Temporal Point Process
Ping Zhang, Rishabh K. Iyer, Ashish V. Tendulkar, Gaurav Aggarwal, Abir De

Meta-learning to Improve Pre-training
Aniruddh Raghu, Jonathan Peter Lorraine, Simon Kornblith, Matthew B.A. McDermott, David Duvenaud

Pointwise Bounds for Distribution Estimation Under Communication Constraints
Wei-Ning Chen, Peter Kairouz, Ayfer Özgür

REMIPS: Physically Consistent 3D Reconstruction of Multiple Interacting People Under Weak Supervision
Mihai Fieraru, Mihai Zanfir, Teodor Alexandru Szente, Eduard Gabriel Bazavan, Vlad Olaru, Cristian Sminchisescu

Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification
Benjamin Eysenbach, Sergey Levine, Ruslan Salakhutdinov

Revealing and Protecting Labels in Distributed Training
Trung Dang, Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, Peter Chin, Françoise Beaufays

Robust Predictable Control
Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine

Robust Visual Reasoning via Language Guided Neural Module Networks
Arjun Reddy Akula, Varun Jampani, Soravit Changpinyo, Song-Chun Zhu

Towards Understanding Retrosynthesis by Energy-Based Models
Ruoxi Sun, Hanjun Dai, Li Li, Steven Kearnes, Bo Dai

Exploring the Limits of Out-of-Distribution Detection
Stanislav Fort, Jie Ren, Balaji Lakshminarayanan

Minimax Regret for Stochastic Shortest Path
Alon Cohen, Yonathan Efroni, Yishay Mansour, Aviv Rosenberg

No Regrets for Learning the Prior in Bandits
Soumya Basu, Branislav Kveton, Manzil Zaheer, Csaba Szepesvari

Structured Denoising Diffusion Models in Discrete State-Spaces
Jacob Austin, Daniel D. Johnsonv, Jonathan Ho, Daniel Tarlow, Rianne van den Berg

The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning (see blog post)
Yujin Tang, David Ha

On the Existence of The Adversarial Bayes Classifier
Pranjal Awasthi, Natalie Frank, Mehyrar Mohri

Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning
Christopher Dann, Teodor Vanislavov Marinov, Mehryar Mohri, Julian Zimmert

A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning
Christopher Dann, Mehryar Mohri, Tong Zhang, Julian Zimmert

Datasets & Benchmarks Accepted Papers

Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research
Bernard Koch, Emily Denton, Alex Hanna, Jacob G. Foster
Datasets & Benchmarks Best Paper

Constructing a Visual Dataset to Study the Effects of Spatial Apartheid in South Africa
Raesetje Sefala, Timnit Gebru, Luzango Mfupe, Nyalleng Moorosi

AI and the Everything in the Whole Wide World Benchmark
Inioluwa Deborah Raji, Emily M. Bender, Amandalynne Paullada, Emily Denton, Alex Hannah

A Unified Few-Shot Classification Benchmark to Compare Transfer and Meta Learning Approaches
Vincent Dumoulin, Neil Houlsby, Utku Evci, Xiaohua Zhai, Ross Goroshin, Sylvain Gelly, Hugo Larochelle

The Neural MMO Platform for Massively Multi-agent Research
Joseph Suarez, Yilun Du, Clare Zhu, Igor Mordatch, Phillip Isola

Systematic Evaluation of Causal Discovery in Visual Model-Based Reinforcement Learning
Nan Rosemary Ke, Aniket Didolkar, Sarthak Mittal, Anirudh Goyal, Guillaume Lajole, Stefan Bauer, Danilo Rezende, Yoshua Bengio, Michael Mozer, Christopher Pal

STEP: Segmenting and Tracking Every Pixel
Mark Weber, Jun Xie, Maxwell Collins, Yukun Zhu, Paul Voigtlaender, Hartwig Adam, Bradley Green, Andreas Geiger, Bastian Leibe, Daneil Cremers, Aljosa Osep, Laura Leal-Taixe, Liang-Chieh Chen

Artsheets for Art Datasets
Ramya Srinivisan, Emily Denton, Jordan Famularo, Negar Rostamzadeh, Fernando Diaz, Beth Coleman

SynthBio: A Case in Human–AI Collaborative Curation of Text Datasets
Ann Yuan, Daphne Ippolito, Vitaly Niolaev, Chris Callison-Burch, Andy Coenen, Sebastian Gehrmann

Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks
Neil Band, Tim G. J. Rudner, Qixuan Feng, Angelos Filos, Zachary Nado, Michael W. Dusenberry, Ghassen Jerfel, Dustin Tran, Yarin Gal

Brax – A Differentiable Physics Engine for Large Scale Rigid Body Simulation (see blog post)
C. Daniel Freeman, Erik Frey, Anton Raichuk, Sertan Girgin, Igor Mordatch, Olivier Bachem

MLPerf Tiny Benchmark
Colby Banbury, Vijay Janapa Reddi, Peter Torelli, Jeremy Holleman, Nat Jeffries, Csaba Kiraly, Pietro Montino, David Kanter, Sebastian Ahmed, Danilo Pau, Urmish Thakker, Antonio Torrini, Peter Warden, Jay Cordaro, Giuseppe Di Guglielmo, Javier Duarte, Stephen Gibellini, Videet Parekh, Honson Tran, Nhan Tran, Niu Wenxu, Xu Xuesong

Automatic Construction of Evaluation Suites for Natural Language Generation Datasets
Simon Mille, Kaustubh D. Dhole, Saad Mahamood, Laura Perez-Beltrachini, Varun Gangal, Mihir Kale, Emiel van Miltenburg, Sebastian Gehrmann

An Empirical Investigation of Representation Learning for Imitation
Xin Chen, Sam Toyer, Cody Wild, Scott Emmons, Ian Fischer, Kuang-Huei Lee, Neel Alex, Steven Wang, Ping Luo, Stuart Russell, Pieter Abbeel, Rohin Shah

Multilingual Spoken Words Corpus
Mark Mazumder, Sharad Chitlangia, Colby Banbury, Yiping Kang, Juan Manuel Ciro, Keith Achorn, Daniel Galvez, Mark Sabini, Peter Mattson, David Kanter, Greg Diamos, Pete Warden, Josh Meyer, Vijay Janapa Reddi

Workshops

4th Robot Learning Workshop: Self-Supervised and Lifelong Learning
Sponsor: Google
Organizers include Alex Bewley, Vincent Vanhoucke

Differentiable Programming Workshop
Sponsor: Google

Machine Learning for Creativity and Design
Sponsor: Google
Organizers include: Daphne Ippolito, David Ha

LatinX in AI (LXAI) Research @ NeurIPS 2021
Sponsor: Google
Sponsorship Level: Platinum
Workshop Chairs include: Andres Munoz Medina
Mentorship Roundtables include: Jonathan Huang, Pablo Samuel Castro

Algorithmic Fairness Through the Lens of Causality and Robustness
Organizers include: Jessica Schrouff, Awa Dieng

ImageNet: Past, Present, and Future
Organizers include: Lucas Beyer, Xiaohua Zhai
Speakers include: Emily Denton, Vittorio Ferrari, Alex Hanna, Alex Kolesnikov, Rebecca Roelofs

Optimal Transport and Machine Learning
Organizers include: Marco Cuturi

Safe and Robust Control of Uncertain Systems
Speakers include: Aleksandra Faust

CtrlGen: Controllable Generative Modeling in Language and Vision
Speakers include: Sebastian Gehrmann

Deep Reinforcement Learning
Organizers include: Chelsea Finn
Speakers include: Karol Hausam, Dale Schuurmans

Distribution Shifts: Connecting Methods and Applications (DistShift)
Speakers include: Chelsea Finn

ML For Systems
Organizers include: Anna Goldie, Martin Maas, Azade Nazi, Azalia Mihoseini, Milad Hashemi, Kevin Swersky

Learning in Presence of Strategic Behavior
Organizers include: Yishay Mansour

Bayesian Deep Learning
Organizers include: Zoubin Ghahramani, Kevin Murphy

Advances in Programming Languages and Neurosymbolic Systems (AIPLANS)
Organizers include: Disha Shrivastava, Vaibhav Tulsyan, Danny Tarlow

Ecological Theory of Reinforcement Learning: How Does Task Design Influence Agent Learning?
Organizers include: Shixiang Shane Gu, Pablo Samuel Castro, Marc G. Bellemare

The Symbiosis of Deep Learning and Differential Equations
Organizers include: Lily Hu

Out-of-Distribution Generalization and Adaptation in Natural and Artificial Intelligence
Speakers include: Chelsea Finn

Cooperative AI
Organizers include: Natasha Jaques

Offline Reinforcement Learning
Organizers include: Rishabh Agarwal, George Tucker
Speakers include: Minmin Chen

2nd Workshop on Self-Supervised Learning: Theory and Practice
Organizers include: Kristina Toutanova

Data Centric AI
Organizers include: Lora Aroyo

Math AI for Education (MATHAI4ED): Bridging the Gap Between Research and Smart Education
Organizers include: Yuhai (Tony) Wu

Tutorials

Beyond Fairness in Machine Learning
Organizers include: Emily Denton

Competitions

Evaluating Approximate Inference in Bayesian Deep Learning
Organizers include: Matthew D. Hoffman, Sharad Vikram

HEAR 2021 NeurIPS Challenge Holistic Evaluation of Audio Representations
Organizers include: Jesse Engel

Machine Learning for Combinatorial Optimization
Organizers include: Pawel Lichocki, Miles Lubin

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^*Work done while at Google.  ^↩
†Currently at Google.  ^↩