Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox
Picture for Michael Rabbat

Michael Rabbat

McGill University, Montreal, Canada

Stochastic Polyak Stepsize with a Moving Target


Jun 22, 2021
Robert M. Gower, Aaron Defazio, Michael Rabbat

Figure 1 for Stochastic Polyak Stepsize with a Moving Target
Figure 2 for Stochastic Polyak Stepsize with a Moving Target
Figure 3 for Stochastic Polyak Stepsize with a Moving Target
Figure 4 for Stochastic Polyak Stepsize with a Moving Target
* 41 pages, 13 figures, 1 table 
  Access Paper or Ask Questions

Federated Learning with Buffered Asynchronous Aggregation


Jun 11, 2021
John Nguyen, Kshitiz Malik, Hongyuan Zhan, Ashkan Yousefpour, Michael Rabbat, Mani Malek Esmaeili, Dzmitry Huba

Figure 1 for Federated Learning with Buffered Asynchronous Aggregation
Figure 2 for Federated Learning with Buffered Asynchronous Aggregation
Figure 3 for Federated Learning with Buffered Asynchronous Aggregation
Figure 4 for Federated Learning with Buffered Asynchronous Aggregation

  Access Paper or Ask Questions

Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples


Apr 28, 2021
Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Armand Joulin, Nicolas Ballas, Michael Rabbat

Figure 1 for Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples
Figure 2 for Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples
Figure 3 for Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples
Figure 4 for Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples

  Access Paper or Ask Questions

A Closer Look at Codistillation for Distributed Training


Oct 06, 2020
Shagun Sodhani, Olivier Delalleau, Mahmoud Assran, Koustuv Sinha, Nicolas Ballas, Michael Rabbat

Figure 1 for A Closer Look at Codistillation for Distributed Training
Figure 2 for A Closer Look at Codistillation for Distributed Training
Figure 3 for A Closer Look at Codistillation for Distributed Training
Figure 4 for A Closer Look at Codistillation for Distributed Training
* Under review 
  Access Paper or Ask Questions

Advances in Asynchronous Parallel and Distributed Optimization


Jun 24, 2020
Mahmoud Assran, Arda Aytekin, Hamid Feyzmahdavian, Mikael Johansson, Michael Rabbat

Figure 1 for Advances in Asynchronous Parallel and Distributed Optimization
Figure 2 for Advances in Asynchronous Parallel and Distributed Optimization
Figure 3 for Advances in Asynchronous Parallel and Distributed Optimization
Figure 4 for Advances in Asynchronous Parallel and Distributed Optimization
* 33 pages, 4 figures 
  Access Paper or Ask Questions

Recovering Petaflops in Contrastive Semi-Supervised Learning of Visual Representations


Jun 18, 2020
Mahmoud Assran, Nicolas Ballas, Lluis Castrejon, Michael Rabbat

Figure 1 for Recovering Petaflops in Contrastive Semi-Supervised Learning of Visual Representations
Figure 2 for Recovering Petaflops in Contrastive Semi-Supervised Learning of Visual Representations
Figure 3 for Recovering Petaflops in Contrastive Semi-Supervised Learning of Visual Representations
Figure 4 for Recovering Petaflops in Contrastive Semi-Supervised Learning of Visual Representations

  Access Paper or Ask Questions

On the Convergence of Nesterov's Accelerated Gradient Method in Stochastic Settings


Feb 27, 2020
Mahmoud Assran, Michael Rabbat

Figure 1 for On the Convergence of Nesterov's Accelerated Gradient Method in Stochastic Settings
Figure 2 for On the Convergence of Nesterov's Accelerated Gradient Method in Stochastic Settings

  Access Paper or Ask Questions

Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge


Jan 06, 2020
Florian Knoll, Tullie Murrell, Anuroop Sriram, Nafissa Yakubova, Jure Zbontar, Michael Rabbat, Aaron Defazio, Matthew J. Muckley, Daniel K. Sodickson, C. Lawrence Zitnick, Michael P. Recht

Figure 1 for Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge
Figure 2 for Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge
Figure 3 for Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge

  Access Paper or Ask Questions

SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum


Oct 01, 2019
Jianyu Wang, Vinayak Tantia, Nicolas Ballas, Michael Rabbat

Figure 1 for SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum
Figure 2 for SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum
Figure 3 for SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum
Figure 4 for SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum

  Access Paper or Ask Questions

A Graph-CNN for 3D Point Cloud Classification


Nov 28, 2018
Yingxue Zhang, Michael Rabbat

Figure 1 for A Graph-CNN for 3D Point Cloud Classification
Figure 2 for A Graph-CNN for 3D Point Cloud Classification
Figure 3 for A Graph-CNN for 3D Point Cloud Classification
Figure 4 for A Graph-CNN for 3D Point Cloud Classification
* Published as a conference paper at ICASSP 2018 
  Access Paper or Ask Questions

Stochastic Gradient Push for Distributed Deep Learning


Nov 27, 2018
Mahmoud Assran, Nicolas Loizou, Nicolas Ballas, Michael Rabbat

Figure 1 for Stochastic Gradient Push for Distributed Deep Learning
Figure 2 for Stochastic Gradient Push for Distributed Deep Learning
Figure 3 for Stochastic Gradient Push for Distributed Deep Learning
Figure 4 for Stochastic Gradient Push for Distributed Deep Learning

  Access Paper or Ask Questions

fastMRI: An Open Dataset and Benchmarks for Accelerated MRI


Nov 21, 2018
Jure Zbontar, Florian Knoll, Anuroop Sriram, Matthew J. Muckley, Mary Bruno, Aaron Defazio, Marc Parente, Krzysztof J. Geras, Joe Katsnelson, Hersh Chandarana, Zizhao Zhang, Michal Drozdzal, Adriana Romero, Michael Rabbat, Pascal Vincent, James Pinkerton, Duo Wang, Nafissa Yakubova, Erich Owens, C. Lawrence Zitnick, Michael P. Recht, Daniel K. Sodickson, Yvonne W. Lui

Figure 1 for fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
Figure 2 for fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
Figure 3 for fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
Figure 4 for fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
* 29 pages, 8 figures 
  Access Paper or Ask Questions

Provably Accelerated Randomized Gossip Algorithms


Oct 31, 2018
Nicolas Loizou, Michael Rabbat, Peter Richtárik

Figure 1 for Provably Accelerated Randomized Gossip Algorithms

  Access Paper or Ask Questions

TarMAC: Targeted Multi-Agent Communication


Oct 26, 2018
Abhishek Das, Théophile Gervet, Joshua Romoff, Dhruv Batra, Devi Parikh, Michael Rabbat, Joelle Pineau

Figure 1 for TarMAC: Targeted Multi-Agent Communication
Figure 2 for TarMAC: Targeted Multi-Agent Communication
Figure 3 for TarMAC: Targeted Multi-Agent Communication
Figure 4 for TarMAC: Targeted Multi-Agent Communication
* 10 pages, 4 figures, 4 tables 
  Access Paper or Ask Questions

Learning Graphs from Data: A Signal Representation Perspective


Jun 03, 2018
Xiaowen Dong, Dorina Thanou, Michael Rabbat, Pascal Frossard

Figure 1 for Learning Graphs from Data: A Signal Representation Perspective
Figure 2 for Learning Graphs from Data: A Signal Representation Perspective
Figure 3 for Learning Graphs from Data: A Signal Representation Perspective
Figure 4 for Learning Graphs from Data: A Signal Representation Perspective

  Access Paper or Ask Questions

Memory vectors for similarity search in high-dimensional spaces


Mar 01, 2017
Ahmet Iscen, Teddy Furon, Vincent Gripon, Michael Rabbat, Hervé Jégou

Figure 1 for Memory vectors for similarity search in high-dimensional spaces
Figure 2 for Memory vectors for similarity search in high-dimensional spaces
Figure 3 for Memory vectors for similarity search in high-dimensional spaces
Figure 4 for Memory vectors for similarity search in high-dimensional spaces
* Accepted to IEEE Transactions on Big Data 
  Access Paper or Ask Questions

Combating Corrupt Messages in Sparse Clustered Associative Memories


Sep 27, 2014
Zhe Yao, Vincent Gripon, Michael Rabbat

Figure 1 for Combating Corrupt Messages in Sparse Clustered Associative Memories
Figure 2 for Combating Corrupt Messages in Sparse Clustered Associative Memories
Figure 3 for Combating Corrupt Messages in Sparse Clustered Associative Memories
Figure 4 for Combating Corrupt Messages in Sparse Clustered Associative Memories

  Access Paper or Ask Questions

Storing sequences in binary tournament-based neural networks


Sep 01, 2014
Xiaoran Jiang, Vincent Gripon, Claude Berrou, Michael Rabbat

Figure 1 for Storing sequences in binary tournament-based neural networks
Figure 2 for Storing sequences in binary tournament-based neural networks
Figure 3 for Storing sequences in binary tournament-based neural networks
Figure 4 for Storing sequences in binary tournament-based neural networks

  Access Paper or Ask Questions

Improving Sparse Associative Memories by Escaping from Bogus Fixed Points


Aug 27, 2013
Zhe Yao, Vincent Gripon, Michael Rabbat

Figure 1 for Improving Sparse Associative Memories by Escaping from Bogus Fixed Points
Figure 2 for Improving Sparse Associative Memories by Escaping from Bogus Fixed Points
Figure 3 for Improving Sparse Associative Memories by Escaping from Bogus Fixed Points
Figure 4 for Improving Sparse Associative Memories by Escaping from Bogus Fixed Points
* 12 pages 
  Access Paper or Ask Questions

GANC: Greedy Agglomerative Normalized Cut


May 05, 2011
Seyed Salim Tabatabaei, Mark Coates, Michael Rabbat

Figure 1 for GANC: Greedy Agglomerative Normalized Cut
Figure 2 for GANC: Greedy Agglomerative Normalized Cut
Figure 3 for GANC: Greedy Agglomerative Normalized Cut
Figure 4 for GANC: Greedy Agglomerative Normalized Cut
* Submitted to Pattern Recognition. 27 pages, 5 figures 
  Access Paper or Ask Questions

Multi-path Probabilistic Available Bandwidth Estimation through Bayesian Active Learning


Jan 06, 2010
Frederic Thouin, Mark Coates, Michael Rabbat

Figure 1 for Multi-path Probabilistic Available Bandwidth Estimation through Bayesian Active Learning
Figure 2 for Multi-path Probabilistic Available Bandwidth Estimation through Bayesian Active Learning
Figure 3 for Multi-path Probabilistic Available Bandwidth Estimation through Bayesian Active Learning
Figure 4 for Multi-path Probabilistic Available Bandwidth Estimation through Bayesian Active Learning

  Access Paper or Ask Questions

Greedy Gossip with Eavesdropping


Sep 09, 2009
Deniz Ustebay, Boris Oreshkin, Mark Coates, Michael Rabbat

Figure 1 for Greedy Gossip with Eavesdropping
Figure 2 for Greedy Gossip with Eavesdropping
Figure 3 for Greedy Gossip with Eavesdropping
Figure 4 for Greedy Gossip with Eavesdropping
* 25 pages, 7 figures 
  Access Paper or Ask Questions