Yoshua Bengio

Research Advisor

Human Decision Support

Leadership team

Recognized worldwide as one of the leading experts in artificial intelligence, Yoshua Bengio is most known for his pioneering work in deep learning, earning him the 2018 A.M. Turing Award, “the Nobel Prize of Computing,” with Geoffrey Hinton and Yann LeCun. He is a Full Professor at Université de Montréal, and the Founder and Scientific Director of Mila – Quebec AI Institute. He co-directs the CIFAR Learning in Machines & Brains program as Senior Fellow, acts as Scientific Director of IVADO and is a Research Advisor at ServiceNow Research. In 2019, he was awarded the prestigious Killam Prize and in 2021, became the second most cited computer scientist in the world. He is a Fellow of both the Royal Society of London and Canada, Knight of the Legion of Honor of France and Officer of the Order of Canada. Concerned about the social impact of AI and the objective that AI benefits all, he actively contributed to the Montreal Declaration for the Responsible Development of Artificial Intelligence.

Interests

Machine Learning
Natural Language Processing
Deep Learning

Publications

Neural Attentive CIrcuits. Martin Weiss, Yoshua Bengio, Nassim, Bernhard Schölkopf, Christopher Pal. At Conference on Neural Information Processing Systems (NeurIPS), 2022.

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Object-centric Compositional Imagination for Visual Abstract Reasoning. Rim Assouel, Perouz Taslakian, David Vazquez, Pau Rodriguez, Yoshua Bengio. At Workshop at the International Conference on Learning Representations (ICLR), 2022.

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VIM: Variational Independent Modules for Video Prediction. Rim Assouel, Lluis Castrejon, Aaron Courville, Nicolas Ballas, Yoshua Bengio. At Causal Learning and Reasoning (CLeaR), 2022.

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Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning. Nan Rosemary Ke, Aniket Didolkar, Sarthak Mittal, Anirudh Goyal, Guillaume Lajoie, Danilo Rezende, Yoshua Bengio, Christopher Pal, Stefan Bauer , Michael C. Mozer. At Conference on Neural Information Processing Systems (NeurIPS), 2021.

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Variational Causal Networks: Approximate Bayesian Inference over Causal Structures. Yashas Annadani, Jonas Rothfuss, Alexandre Lacoste, Nino Scherrer, Anirudh Goyal, Yoshua Bengio, Stefan Bauer . At Workshop at the International Conference on Machine Learning (ICML), 2021.

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Predicting Infectiousness for Proactive Contact Tracing. Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif Benjamin Muller, Meng Qu, victor schmidt, Pierre-luc St-charles, hannah alsdurf, Olexa Bilaniuk, david buckeridge, gaetan caron, pierre luc carrier, Joumana Ghosn, satya ortiz gagne, Christopher Pal, Irina Rish, Bernhard Schölkopf, abhinav sharma, Jian Tang, andrew williams. At International Conference on Learning Representations (ICLR), 2021.

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A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms. Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Nan Rosemary Ke, Sébastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, Christopher Pal. At International Conference on Learning Representations (ICLR), 2020.

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N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, Yoshua Bengio. At International Conference on Learning Representations (ICLR), 2020.

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The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget. Anirudh Goyal, Yoshua Bengio, Matthew Botvinick, Sergey Levine. At International Conference on Learning Representations (ICLR), 2020.

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HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery. Michel Deudon, Alfredo Kalaitzis, Israel Goytom, Md Rifat Arefin, Zhichao Lin, Kris Sankaran, Vincent Michalski, Samira E. Kahou, Julien Cornebise, Yoshua Bengio. At ArXiv, 2020.

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CLOSURE: Assessing Systematic Generalization of CLEVR models. Dzmitry Bahdanau, Harm de Vries, Timothy J. O'Donnell, Shikhar Murty, Philippe Beaudoin, Yoshua Bengio, Aaron Courville. At Workshop at the Neural Information Processing Systems (NeurIPS), 2019.

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On Adversarial Mixup Resynthesis. Christopher Beckham, Sina Honari, Vikas Verma, Alex Lamb, Farnoosh Ghadiri, R Devon Hjelm, Yoshua Bengio, Christopher Pal. At Conference on Neural Information Processing Systems (NeurIPS), 2019.

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Tackling Climate Change with Machine Learning. David Rolnick, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Luccioni, Tegan Maharaj, S. Karthik Mukkavilli, Konrad P. Kording, Carla Gomes, Andrew Y. Ng, Demis Hassabis, John C. Platt, Felix Creutzig, Jennifer Chayes, Yoshua Bengio, Evan D. Sherwin. At Workshop at the Neural Information Processing Systems (NeurIPS), 2019.

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Adversarial Mixup Resynthesizers. Christopher Beckham, Sina Honari, Vikas Verma, Alex Lamb, Farnoosh Ghadiri, R Devon Hjelm, Yoshua Bengio, Christopher Pal. At Workshop at the International Conference on Learning Representations (ICLR), 2019.

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BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning. Maxime Chevalier-Boisvert, Dzmitry Bahdanau, Salem Lahlou, Lucas Willems, Chitwan Saharia, Thien Huu Nguyen, Yoshua Bengio. At International Conference on Learning Representations (ICLR), 2019.

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Meta-Learning Framework with Applications to Zero-Shot Time Series Forecasting. Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, Yoshua Bengio. At International Conference on Learning Representations (ICLR), 2019.

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Probabilistic Planning with Sequential Monte Carlo Methods. Alexandre Piche, Valentin Thomas, Cyril Ibrahim, Yoshua Bengio, Christopher Pal. At International Conference on Learning Representations (ICLR), 2019.

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Quaternion Recurrent Neural Networks. Titouan Parcollet, Mirco Ravanelli, Mohamed Morchid, Georges Linarès, Chiheb Trabelsi, Renato De Mori, Yoshua Bengio. At International Conference on Learning Representations (ICLR), 2019.

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Towards Standardization of Data Licenses: The Montreal Data License. Misha Benjamin, Paul Gagnon, Negar Rostamzadeh, Christopher Pal, Yoshua Bengio, Alex Shee. At Workshop at the International Conference on Learning Representations (ICLR), 2019.

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Bayesian Model-Agnostic Meta-Learning. Taesup Kim, Jaesik Yoon, Sungwoong Kim, Yoshua Bengio, Sungjin Ahn. At Conference on Neural Information Processing Systems (NeurIPS), 2018.

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Reinforced Imitation in Heterogeneous Action Space. Konrad Zolna, Negar Rostamzadeh, Yoshua Bengio, Sungjin Ahn, Pedro O. Pinheiro. At Workshop at the Neural Information Processing Systems (NeurIPS), 2018.

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Sparse Attentive Backtracking: Temporal Credit Assignment Through Reminding. Nan Rosemary Ke, Anirudh Goyal, Olexa Bilaniuk, Jonathan Binas, Christopher Pal, Yoshua Bengio, Michael C. Mozer. At Conference on Neural Information Processing Systems (NeurIPS), 2018.

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Quaternion Convolutional Neural Networks for End-to-End Automatic Speech Recognition. Titouan Parcollet, Ying Zhang, Mohamed Morchid, Chiheb Trabelsi, Georges Linarès, Renato De Mori, Yoshua Bengio. At Interspeech Conference, 2018.

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Bayesian Model-Agnostic Meta-Learning. Taesup Kim, Jaesik Yoon, Ousmane Amadou Dia, Sungwoong Kim, Yoshua Bengio, Sungjin Ahn. At Montreal AI Symposium (MAIS), 2018.

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Deep Complex Networks. Chiheb Trabelsi, Olexa Bilaniuk, Ying Zhang, Dmitriy Serdyuk, Sandeep Subramanian, João Felipe Santos, Soroush Mehri, Negar Rostamzadeh, Yoshua Bengio, Christopher Pal. At International Conference on Learning Representations (ICLR), 2018.

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Disentangling the independently controllable factors of variation by interacting with the world. Valentin Thomas, Emmanuel Bengio, William Fedus, Jules Pondard, Philippe Beaudoin, Hugo Larochelle, Joelle Pineau, Doina Precup, Yoshua Bengio. At Workshop at the Neural Information Processing Systems (NeurIPS), 2017.

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Independently Controllable Factors. Valentin Thomas, Jules Pondard, Emmanuel Bengio, Marc Sarfati, Philippe Beaudoin, Marie-Jean Meurs, Joelle Pineau, Doina Precup, Yoshua Bengio. At ArXiv, 2017.

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