Portrait of Yoshua Bengio

Yoshua Bengio

Core Academic Member
Canada CIFAR AI Chair
Full Professor, Université de Montréal, Department of Computer Science and Operations Research Department
Founder and Scientific Advisor, Leadership Team
Research Topics
Causality
Computational Neuroscience
Deep Learning
Generative Models
Graph Neural Networks
Machine Learning Theory
Medical Machine Learning
Molecular Modeling
Natural Language Processing
Probabilistic Models
Reasoning
Recurrent Neural Networks
Reinforcement Learning
Representation Learning
Website
Google Scholar

Biography

*For media requests, please write to medias@mila.quebec.

For more information please contact Marie-Josée Beauchamp, Administrative Assistant at marie-josee.beauchamp@mila.quebec.

Yoshua Bengio is recognized worldwide as a leading expert in AI. He is most known for his pioneering work in deep learning, which earned him the 2018 A.M. Turing Award, “the Nobel Prize of computing,” with Geoffrey Hinton and Yann LeCun.

Bengio is a full professor at Université de Montréal, and the founder and scientific advisor of Mila – Quebec Artificial Intelligence Institute. He is also a senior fellow at CIFAR and co-directs its Learning in Machines & Brains program, serves as special advisor and founding scientific director of IVADO, and holds a Canada CIFAR AI Chair.

In 2019, Bengio was awarded the prestigious Killam Prize and in 2022, he was the most cited computer scientist in the world by h-index. He is a Fellow of the Royal Society of London, Fellow of the Royal Society of Canada, Knight of the Legion of Honor of France and Officer of the Order of Canada. In 2023, he was appointed to the UN’s Scientific Advisory Board for Independent Advice on Breakthroughs in Science and Technology.

Concerned about the social impact of AI, Bengio helped draft the Montréal Declaration for the Responsible Development of Artificial Intelligence and continues to raise awareness about the importance of mitigating the potentially catastrophic risks associated with future AI systems.

Current Students

Jamal Abou Haibeh
Collaborating Alumni - McGill University
Github
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Mohammed Abukalam
Collaborating Alumni - Université de Montréal
Github
Berkes Anaïs
Collaborating researcher - Cambridge University
Principal supervisor :
David Rolnick
Website
Github
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Rim Assouel
PhD - Université de Montréal
Junyeob BAEK
Independent visiting researcher - KAIST
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Stefan Bauer
Independent visiting researcher
Co-supervisor :
Guillaume Lajoie
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Paul Bertin
PhD - Université de Montréal
Shahana Chatterjee
Collaborating researcher - N/A
Principal supervisor :
David Rolnick
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Xiaoyin Chen
PhD - Université de Montréal
Sanghyeok Choi
Collaborating researcher - KAIST
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Tristan Deleu
PhD - Université de Montréal
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Aniket Didolkar
PhD - Université de Montréal
Website
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Abdessamad EL KABID
Research Intern - Université de Montréal
Co-supervisor :
Loubna Benabbou
Github
Eric Elmoznino
PhD - Université de Montréal
Co-supervisor :
Guillaume Lajoie
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Léna Ezzine
PhD - Université de Montréal
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Jean-Pierre Falet
PhD - Université de Montréal
Co-supervisor :
Guillaume Lajoie
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Leo Feng
PhD - Université de Montréal
leo.feng@mila.quebec
Website
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Ivan Grega
Research Intern - Université de Montréal
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Pietro Greiner
PhD
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Mohsin Hasan
PhD - Université de Montréal
mohsin.hasan@mila.quebec
Website
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Edward Hu
PhD - Université de Montréal
Moksh Jain
PhD - Université de Montréal
moksh.jain@mila.quebec
Website
Github
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Thomas Jiralerspong
PhD - Université de Montréal
Principal supervisor :
Guillaume Lajoie
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Younesse Kaddar
Collaborating Alumni - Université de Montréal
Website
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Hyeonah Kim
Postdoctorate - Université de Montréal
Principal supervisor :
Alex Hernandez
Yaroslav KIVVA
Collaborating researcher - Université de Montréal
Github
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Salem Lahlou
Collaborating Alumni - Université de Montréal
Github
Tabitha Edith Lee
Postdoctorate - Université de Montréal
Principal supervisor :
Glen Berseth
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Seanie Lee
Collaborating Alumni - Université de Montréal
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Zhen Liu
Collaborating Alumni - Université de Montréal
Principal supervisor :
Liam Paull
Chenghao Liu
Collaborating Alumni
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Kanika Madan
PhD - Université de Montréal
Nikolay Malkin
Collaborating Alumni - Université de Montréal
Website
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Cristian Dragos Manta
PhD - Université de Montréal
Co-supervisor :
Dhanya Sridhar
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Sören Mindermann
Collaborating researcher - Université de Montréal
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Sarthak Mittal
PhD - Université de Montréal
Principal supervisor :
Guillaume Lajoie
Website
Github
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Jama Mohamud
PhD - Université de Montréal
Principal supervisor :
Mirco Ravanelli
Website
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Cyrus Neary
Postdoctorate - Université de Montréal
Principal supervisor :
Glen Berseth
Website
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Phong Nguyen
Independent visiting researcher - Université de Montréal
Padideh Nouri
PhD - Université de Montréal
Principal supervisor :
Doina Precup
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Ali Parviz
Collaborating researcher - Ying Wu Coll of Computing
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Lena Podina
PhD - University of Waterloo
Principal supervisor :
David Rolnick
Github
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Nassim Rahaman
Collaborating Alumni - Max-Planck-Institute for Intelligent Systems
Amine RAZIG
Research Intern - Université de Montréal
Co-supervisor :
Loubna Benabbou
Website
Jarrid Rector-Brooks
PhD - Université de Montréal
Danyal REHMAN
Postdoctorate - Université de Montréal
Github
James Requeima
Independent visiting researcher - Université de Montréal
Oli RICHARDSON
Postdoctorate - Université de Montréal
Website
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Camille Rochefort-Boulanger
PhD - Université de Montréal
Principal supervisor :
Julie Hussin
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Victor Schmidt
Collaborating Alumni - Université de Montréal
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Luca Scimeca
Postdoctorate - Université de Montréal
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Dragos Secrieru
Master's Research - Université de Montréal
Marcin Sendera
Collaborating Alumni - Université de Montréal
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Vedant Shah
Master's Research - Université de Montréal
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Divya Sharma
Postdoctorate
Website
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Marco Stock
Independent visiting researcher - Technical University of Munich
marco.stock@tum.de
Github
Mélisande Astrid Crystal Teng
PhD - Université de Montréal
Co-supervisor :
Hugo Larochelle
Website
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Alex Tong
Postdoctorate - Université de Montréal
alexander.tong@mila.quebec
Website
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Donna Vakalis
Postdoctorate - Université de Montréal
Co-supervisor :
David Rolnick
Website
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Siddarth Venkatraman
PhD - Université de Montréal
Principal supervisor :
Glen Berseth
Website
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Leah WAIRIMU
Collaborating researcher - Université de Montréal
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Omar G. Younis
Collaborating researcher
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Taeyoung YUN
Collaborating researcher - KAIST
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Tianyu Zhang
PhD - Université de Montréal
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Nicole Zhang
PhD - McGill University
Principal supervisor :
Mathieu Blanchette
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Dinghuai Zhang
PhD - Université de Montréal
Principal supervisor :
Aaron Courville
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Harry Zhao
PhD - McGill University
Principal supervisor :
Doina Precup
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Publications

Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning
Dianbo Liu
Vedant Shah
Oussama Boussif
Cristian Meo
Anirudh Goyal
Tianmin Shu
Michael Curtis Mozer
Nicolas Heess
Yoshua Bengio
2023-02-01
ICLR.cc/2023/Conference (poster)
doi.org
openreview.net
A theory of continuous generative flow networks
Salem Lahlou
Tristan Deleu
Pablo Lemos
Dinghuai Zhang
Alexandra Volokhova
Alex Hernandez-Garcia
Lena Nehale Ezzine
Yoshua Bengio
Nikolay Malkin
Generative flow networks (GFlowNets) are amortized variational inference algorithms that are trained to sample from unnormalized target dist… (see more)ributions over compositional objects. A key limitation of GFlowNets until this time has been that they are restricted to discrete spaces. We present a theory for generalized GFlowNets, which encompasses both existing discrete GFlowNets and ones with continuous or hybrid state spaces, and perform experiments with two goals in mind. First, we illustrate critical points of the theory and the importance of various assumptions. Second, we empirically demonstrate how observations about discrete GFlowNets transfer to the continuous case and show strong results compared to non-GFlowNet baselines on several previously studied tasks. This work greatly widens the perspectives for the application of GFlowNets in probabilistic inference and various modeling settings.
2023-01-30
ArXiv (preprint)
doi.org
arxiv.org
Leveraging the Third Dimension in Contrastive Learning
Sumukh K Aithal
Anirudh Goyal
Alex Lamb
Yoshua Bengio
Michael Curtis Mozer
Self-Supervised Learning (SSL) methods operate on unlabeled data to learn robust representations useful for downstream tasks. Most SSL metho… (see more)ds rely on augmentations obtained by transforming the 2D image pixel map. These augmentations ignore the fact that biological vision takes place in an immersive three-dimensional, temporally contiguous environment, and that low-level biological vision relies heavily on depth cues. Using a signal provided by a pretrained state-of-the-art monocular RGB-to-depth model (the \emph{Depth Prediction Transformer}, Ranftl et al., 2021), we explore two distinct approaches to incorporating depth signals into the SSL framework. First, we evaluate contrastive learning using an RGB+depth input representation. Second, we use the depth signal to generate novel views from slightly different camera positions, thereby producing a 3D augmentation for contrastive learning. We evaluate these two approaches on three different SSL methods -- BYOL, SimSiam, and SwAV -- using ImageNette (10 class subset of ImageNet), ImageNet-100 and ImageNet-1k datasets. We find that both approaches to incorporating depth signals improve the robustness and generalization of the baseline SSL methods, though the first approach (with depth-channel concatenation) is superior. For instance, BYOL with the additional depth channel leads to an increase in downstream classification accuracy from 85.3\% to 88.0\% on ImageNette and 84.1\% to 87.0\% on ImageNet-C.
2023-01-27
ArXiv (preprint)
doi.org
openreview.net
Regeneration Learning: A Learning Paradigm for Data Generation
Xu Tan
Tao Qin
Jiang Bian
Tie-Yan Liu
Yoshua Bengio
2023-01-21
ArXiv (preprint)
doi.org
arxiv.org
Benchmarking Graph Neural Networks
Vijay Prakash Dwivedi
Chaitanya K. Joshi
Thomas Laurent
Yoshua Bengio
Xavier Bresson
Graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs. As the field grows, it becomes… (see more) critical to identify key architectures and validate new ideas that generalize to larger, more complex datasets. Unfortunately, it has been increasingly difficult to gauge the effectiveness of new models in the absence of a standardized benchmark with consistent experimental settings. In this paper, we introduce a reproducible GNN benchmarking framework, with the facility for researchers to add new models conveniently for arbitrary datasets. We demonstrate the usefulness of our framework by presenting a principled investigation into the recent Weisfeiler-Lehman GNNs (WL-GNNs) compared to message passing-based graph convolutional networks (GCNs) for a variety of graph tasks, i.e. graph regression/classification and node/link prediction, with medium-scale datasets.
2023-01-01
ArXiv (preprint)
arxiv.org
arxiv.org
Benchmarking Graph Neural Networks
Vijay Prakash Dwivedi
Chaitanya K. Joshi
Thomas Laurent
Yoshua Bengio
Xavier Bresson
Graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs. As the field grows, it becomes… (see more) critical to identify key architectures and validate new ideas that generalize to larger, more complex datasets. Unfortunately, it has been increasingly difficult to gauge the effectiveness of new models in the absence of a standardized benchmark with consistent experimental settings. In this paper, we introduce a reproducible GNN benchmarking framework, with the facility for researchers to add new models conveniently for arbitrary datasets. We demonstrate the usefulness of our framework by presenting a principled investigation into the recent Weisfeiler-Lehman GNNs (WL-GNNs) compared to message passing-based graph convolutional networks (GCNs) for a variety of graph tasks, i.e. graph regression/classification and node/link prediction, with medium-scale datasets.
2023-01-01
ArXiv (preprint)
arxiv.org
arxiv.org
Benchmarking Graph Neural Networks
Vijay Prakash Dwivedi
Chaitanya K. Joshi
Thomas Laurent
Yoshua Bengio
Xavier Bresson
Graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs. As the field grows, it becomes… (see more) critical to identify key architectures and validate new ideas that generalize to larger, more complex datasets. Unfortunately, it has been increasingly difficult to gauge the effectiveness of new models in the absence of a standardized benchmark with consistent experimental settings. In this paper, we introduce a reproducible GNN benchmarking framework, with the facility for researchers to add new models conveniently for arbitrary datasets. We demonstrate the usefulness of our framework by presenting a principled investigation into the recent Weisfeiler-Lehman GNNs (WL-GNNs) compared to message passing-based graph convolutional networks (GCNs) for a variety of graph tasks, i.e. graph regression/classification and node/link prediction, with medium-scale datasets.
2023-01-01
ArXiv (preprint)
arxiv.org
arxiv.org
Benchmarking Graph Neural Networks
Vijay Prakash Dwivedi
Chaitanya K. Joshi
Thomas Laurent
Yoshua Bengio
Xavier Bresson
Graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs. As the field grows, it becomes… (see more) critical to identify key architectures and validate new ideas that generalize to larger, more complex datasets. Unfortunately, it has been increasingly difficult to gauge the effectiveness of new models in the absence of a standardized benchmark with consistent experimental settings. In this paper, we introduce a reproducible GNN benchmarking framework, with the facility for researchers to add new models conveniently for arbitrary datasets. We demonstrate the usefulness of our framework by presenting a principled investigation into the recent Weisfeiler-Lehman GNNs (WL-GNNs) compared to message passing-based graph convolutional networks (GCNs) for a variety of graph tasks, i.e. graph regression/classification and node/link prediction, with medium-scale datasets.
2023-01-01
ArXiv (preprint)
arxiv.org
arxiv.org
Benchmarking Graph Neural Networks
Vijay Prakash Dwivedi
Chaitanya K. Joshi
Thomas Laurent
Anh Tuan Luu
Yoshua Bengio
Xavier Bresson
arxiv.org
Benchmarking Graph Neural Networks
Vijay Prakash Dwivedi
Chaitanya K. Joshi
Thomas Laurent
Yoshua Bengio
Xavier Bresson
Graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs. As the field grows, it becomes… (see more) critical to identify key architectures and validate new ideas that generalize to larger, more complex datasets. Unfortunately, it has been increasingly difficult to gauge the effectiveness of new models in the absence of a standardized benchmark with consistent experimental settings. In this paper, we introduce a reproducible GNN benchmarking framework, with the facility for researchers to add new models conveniently for arbitrary datasets. We demonstrate the usefulness of our framework by presenting a principled investigation into the recent Weisfeiler-Lehman GNNs (WL-GNNs) compared to message passing-based graph convolutional networks (GCNs) for a variety of graph tasks, i.e. graph regression/classification and node/link prediction, with medium-scale datasets.
2023-01-01
ArXiv (preprint)
arxiv.org
arxiv.org
Benchmarking Graph Neural Networks
Vijay Prakash Dwivedi
Chaitanya K. Joshi
Thomas Laurent
Yoshua Bengio
Xavier Bresson
Graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs. As the field grows, it becomes… (see more) critical to identify key architectures and validate new ideas that generalize to larger, more complex datasets. Unfortunately, it has been increasingly difficult to gauge the effectiveness of new models in the absence of a standardized benchmark with consistent experimental settings. In this paper, we introduce a reproducible GNN benchmarking framework, with the facility for researchers to add new models conveniently for arbitrary datasets. We demonstrate the usefulness of our framework by presenting a principled investigation into the recent Weisfeiler-Lehman GNNs (WL-GNNs) compared to message passing-based graph convolutional networks (GCNs) for a variety of graph tasks, i.e. graph regression/classification and node/link prediction, with medium-scale datasets.
2023-01-01
ArXiv (preprint)
arxiv.org
arxiv.org
Benchmarking Graph Neural Networks
Vijay Prakash Dwivedi
Chaitanya K. Joshi
Thomas Laurent
Yoshua Bengio
Xavier Bresson
Graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs. As the field grows, it becomes… (see more) critical to identify key architectures and validate new ideas that generalize to larger, more complex datasets. Unfortunately, it has been increasingly difficult to gauge the effectiveness of new models in the absence of a standardized benchmark with consistent experimental settings. In this paper, we introduce a reproducible GNN benchmarking framework, with the facility for researchers to add new models conveniently for arbitrary datasets. We demonstrate the usefulness of our framework by presenting a principled investigation into the recent Weisfeiler-Lehman GNNs (WL-GNNs) compared to message passing-based graph convolutional networks (GCNs) for a variety of graph tasks, i.e. graph regression/classification and node/link prediction, with medium-scale datasets.
2023-01-01
ArXiv (preprint)
arxiv.org
arxiv.org