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 Vincent Michalski

Vincent Michalski

Accounting for Variance in Machine Learning Benchmarks


Mar 01, 2021
Xavier Bouthillier, Pierre Delaunay, Mirko Bronzi, Assya Trofimov, Brennan Nichyporuk, Justin Szeto, Naz Sepah, Edward Raff, Kanika Madan, Vikram Voleti, Samira Ebrahimi Kahou, Vincent Michalski, Dmitriy Serdyuk, Tal Arbel, Chris Pal, Gaël Varoquaux, Pascal Vincent

Figure 1 for Accounting for Variance in Machine Learning Benchmarks
Figure 2 for Accounting for Variance in Machine Learning Benchmarks
Figure 3 for Accounting for Variance in Machine Learning Benchmarks
Figure 4 for Accounting for Variance in Machine Learning Benchmarks
* Submitted to MLSys2021 
  Access Paper or Ask Questions

HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery


Feb 15, 2020
Michel Deudon, Alfredo Kalaitzis, Israel Goytom, Md Rifat Arefin, Zhichao Lin, Kris Sankaran, Vincent Michalski, Samira E. Kahou, Julien Cornebise, Yoshua Bengio

Figure 1 for HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery
Figure 2 for HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery
Figure 3 for HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery
Figure 4 for HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery
* 15 pages, 5 figures 
  Access Paper or Ask Questions

An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation


Jul 31, 2019
Vincent Michalski, Vikram Voleti, Samira Ebrahimi Kahou, Anthony Ortiz, Pascal Vincent, Chris Pal, Doina Precup

Figure 1 for An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation
Figure 2 for An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation
Figure 3 for An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation
Figure 4 for An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation

  Access Paper or Ask Questions

Towards Deep Conversational Recommendations


Dec 18, 2018
Raymond Li, Samira Kahou, Hannes Schulz, Vincent Michalski, Laurent Charlin, Chris Pal

Figure 1 for Towards Deep Conversational Recommendations
Figure 2 for Towards Deep Conversational Recommendations
Figure 3 for Towards Deep Conversational Recommendations
Figure 4 for Towards Deep Conversational Recommendations
* 17 pages, 5 figures, Accepted at 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montr\'eal, Canada 
  Access Paper or Ask Questions

FigureQA: An Annotated Figure Dataset for Visual Reasoning


Feb 22, 2018
Samira Ebrahimi Kahou, Vincent Michalski, Adam Atkinson, Akos Kadar, Adam Trischler, Yoshua Bengio

Figure 1 for FigureQA: An Annotated Figure Dataset for Visual Reasoning
Figure 2 for FigureQA: An Annotated Figure Dataset for Visual Reasoning
Figure 3 for FigureQA: An Annotated Figure Dataset for Visual Reasoning
Figure 4 for FigureQA: An Annotated Figure Dataset for Visual Reasoning
* workshop paper at ICLR 2018 
  Access Paper or Ask Questions

ChatPainter: Improving Text to Image Generation using Dialogue


Feb 22, 2018
Shikhar Sharma, Dendi Suhubdy, Vincent Michalski, Samira Ebrahimi Kahou, Yoshua Bengio

Figure 1 for ChatPainter: Improving Text to Image Generation using Dialogue
Figure 2 for ChatPainter: Improving Text to Image Generation using Dialogue
Figure 3 for ChatPainter: Improving Text to Image Generation using Dialogue
Figure 4 for ChatPainter: Improving Text to Image Generation using Dialogue

  Access Paper or Ask Questions

A Deep Reinforcement Learning Chatbot (Short Version)


Jan 20, 2018
Iulian V. Serban, Chinnadhurai Sankar, Mathieu Germain, Saizheng Zhang, Zhouhan Lin, Sandeep Subramanian, Taesup Kim, Michael Pieper, Sarath Chandar, Nan Rosemary Ke, Sai Rajeswar, Alexandre de Brebisson, Jose M. R. Sotelo, Dendi Suhubdy, Vincent Michalski, Alexandre Nguyen, Joelle Pineau, Yoshua Bengio

Figure 1 for A Deep Reinforcement Learning Chatbot (Short Version)
Figure 2 for A Deep Reinforcement Learning Chatbot (Short Version)
Figure 3 for A Deep Reinforcement Learning Chatbot (Short Version)
* 9 pages, 1 figure, 2 tables; presented at NIPS 2017, Conversational AI: "Today's Practice and Tomorrow's Potential" Workshop 
  Access Paper or Ask Questions

A Deep Reinforcement Learning Chatbot


Nov 05, 2017
Iulian V. Serban, Chinnadhurai Sankar, Mathieu Germain, Saizheng Zhang, Zhouhan Lin, Sandeep Subramanian, Taesup Kim, Michael Pieper, Sarath Chandar, Nan Rosemary Ke, Sai Rajeshwar, Alexandre de Brebisson, Jose M. R. Sotelo, Dendi Suhubdy, Vincent Michalski, Alexandre Nguyen, Joelle Pineau, Yoshua Bengio

Figure 1 for A Deep Reinforcement Learning Chatbot
Figure 2 for A Deep Reinforcement Learning Chatbot
Figure 3 for A Deep Reinforcement Learning Chatbot
Figure 4 for A Deep Reinforcement Learning Chatbot
* 40 pages, 9 figures, 11 tables 
  Access Paper or Ask Questions

The "something something" video database for learning and evaluating visual common sense


Jun 15, 2017
Raghav Goyal, Samira Ebrahimi Kahou, Vincent Michalski, Joanna Materzyńska, Susanne Westphal, Heuna Kim, Valentin Haenel, Ingo Fruend, Peter Yianilos, Moritz Mueller-Freitag, Florian Hoppe, Christian Thurau, Ingo Bax, Roland Memisevic

Figure 1 for The "something something" video database for learning and evaluating visual common sense
Figure 2 for The "something something" video database for learning and evaluating visual common sense
Figure 3 for The "something something" video database for learning and evaluating visual common sense
Figure 4 for The "something something" video database for learning and evaluating visual common sense

  Access Paper or Ask Questions

Theano: A Python framework for fast computation of mathematical expressions


May 09, 2016
The Theano Development Team, Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermueller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, Alexander Belopolsky, Yoshua Bengio, Arnaud Bergeron, James Bergstra, Valentin Bisson, Josh Bleecher Snyder, Nicolas Bouchard, Nicolas Boulanger-Lewandowski, Xavier Bouthillier, Alexandre de Brébisson, Olivier Breuleux, Pierre-Luc Carrier, Kyunghyun Cho, Jan Chorowski, Paul Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Mélanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian Goodfellow, Matt Graham, Caglar Gulcehre, Philippe Hamel, Iban Harlouchet, Jean-Philippe Heng, Balázs Hidasi, Sina Honari, Arjun Jain, Sébastien Jean, Kai Jia, Mikhail Korobov, Vivek Kulkarni, Alex Lamb, Pascal Lamblin, Eric Larsen, César Laurent, Sean Lee, Simon Lefrancois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert T. McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher Pal, Razvan Pascanu, Mohammad Pezeshki, Colin Raffel, Daniel Renshaw, Matthew Rocklin, Adriana Romero, Markus Roth, Peter Sadowski, John Salvatier, François Savard, Jan Schlüter, John Schulman, Gabriel Schwartz, Iulian Vlad Serban, Dmitriy Serdyuk, Samira Shabanian, Étienne Simon, Sigurd Spieckermann, S. Ramana Subramanyam, Jakub Sygnowski, Jérémie Tanguay, Gijs van Tulder, Joseph Turian, Sebastian Urban, Pascal Vincent, Francesco Visin, Harm de Vries, David Warde-Farley, Dustin J. Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, Ying Zhang

Figure 1 for Theano: A Python framework for fast computation of mathematical expressions
Figure 2 for Theano: A Python framework for fast computation of mathematical expressions
Figure 3 for Theano: A Python framework for fast computation of mathematical expressions
Figure 4 for Theano: A Python framework for fast computation of mathematical expressions
* 19 pages, 5 figures 
  Access Paper or Ask Questions

RATM: Recurrent Attentive Tracking Model


Apr 28, 2016
Samira Ebrahimi Kahou, Vincent Michalski, Roland Memisevic

Figure 1 for RATM: Recurrent Attentive Tracking Model

  Access Paper or Ask Questions

EmoNets: Multimodal deep learning approaches for emotion recognition in video


Mar 30, 2015
Samira Ebrahimi Kahou, Xavier Bouthillier, Pascal Lamblin, Caglar Gulcehre, Vincent Michalski, Kishore Konda, Sébastien Jean, Pierre Froumenty, Yann Dauphin, Nicolas Boulanger-Lewandowski, Raul Chandias Ferrari, Mehdi Mirza, David Warde-Farley, Aaron Courville, Pascal Vincent, Roland Memisevic, Christopher Pal, Yoshua Bengio

Figure 1 for EmoNets: Multimodal deep learning approaches for emotion recognition in video
Figure 2 for EmoNets: Multimodal deep learning approaches for emotion recognition in video
Figure 3 for EmoNets: Multimodal deep learning approaches for emotion recognition in video
Figure 4 for EmoNets: Multimodal deep learning approaches for emotion recognition in video

  Access Paper or Ask Questions

Modeling sequential data using higher-order relational features and predictive training


Feb 10, 2014
Vincent Michalski, Roland Memisevic, Kishore Konda

Figure 1 for Modeling sequential data using higher-order relational features and predictive training
Figure 2 for Modeling sequential data using higher-order relational features and predictive training
Figure 3 for Modeling sequential data using higher-order relational features and predictive training
Figure 4 for Modeling sequential data using higher-order relational features and predictive training

  Access Paper or Ask Questions

Learning to encode motion using spatio-temporal synchrony


Feb 10, 2014
Kishore Reddy Konda, Roland Memisevic, Vincent Michalski

Figure 1 for Learning to encode motion using spatio-temporal synchrony
Figure 2 for Learning to encode motion using spatio-temporal synchrony
Figure 3 for Learning to encode motion using spatio-temporal synchrony
Figure 4 for Learning to encode motion using spatio-temporal synchrony

  Access Paper or Ask Questions