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
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Mohammed Abukalam
Collaborating Alumni - Université de Montréal
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Berkes Anaïs
Collaborating researcher - Cambridge University
Principal supervisor :
David Rolnick
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Rim Assouel
PhD - Université de Montréal
Stefan Bauer
Independent visiting researcher
Co-supervisor :
Guillaume Lajoie
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Paul Bertin
PhD - Université de Montréal
Joyce Chai
Independent visiting researcher
Principal supervisor :
Siva Reddy
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
Collaborating Alumni - Université de Montréal
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Aniket Didolkar
PhD - Université de Montréal
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Abdessamad EL KABID
Collaborating Alumni - Université de Montréal
Co-supervisor :
Loubna Benabbou
Desmond Elliott
Independent visiting researcher
Principal supervisor :
Siva Reddy
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Eric Elmoznino
PhD - Université de Montréal
Co-supervisor :
Guillaume Lajoie
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Léna Ezzine
PhD - Université de Montréal
Jean-Pierre Falet
PhD - Université de Montréal
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Leo Feng
PhD - Université de Montréal
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Pietro Greiner
PhD
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Mohsin Hasan
PhD - Université de Montréal
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Edward Hu
PhD - Université de Montréal
Moksh Jain
PhD - Université de Montréal
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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
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Hyeonah Kim
Postdoctorate - Université de Montréal
Principal supervisor :
Alex Hernandez-Garcia
Salem Lahlou
Collaborating Alumni - Université de Montréal
Tabitha Edith Lee
Postdoctorate - Université de Montréal
Principal supervisor :
Glen Berseth
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Chenghao Liu
Collaborating Alumni
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Zhen Liu
Collaborating Alumni - Université de Montréal
Principal supervisor :
Liam Paull
Kanika Madan
PhD - Université de Montréal
Nikolay Malkin
Collaborating Alumni - Université de Montréal
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Cristian Dragos Manta
PhD - Université de Montréal
Co-supervisor :
Dhanya Sridhar
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Sarthak Mittal
PhD - Université de Montréal
Principal supervisor :
Guillaume Lajoie
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Jama Mohamud
PhD - Université de Montréal
Principal supervisor :
Mirco Ravanelli
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Cyrus Neary
Postdoctorate - Université de Montréal
Principal supervisor :
Glen Berseth
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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
Collaborating researcher - University of Waterloo
Principal supervisor :
David Rolnick
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Nassim Rahaman
Collaborating Alumni - Max-Planck-Institute for Intelligent Systems
Amine RAZIG
Collaborating researcher - Université de Montréal
Co-supervisor :
Loubna Benabbou
Website
Jarrid Rector-Brooks
PhD - Université de Montréal
Danyal REHMAN
Postdoctorate - Université de Montréal
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James Requeima
Independent visiting researcher - Université de Montréal
Oli RICHARDSON
Postdoctorate - Université de Montréal
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Camille Rochefort-Boulanger
PhD - Université de Montréal
Principal supervisor :
Julie Hussin
Github
Abhik Roychoudhury Roychoudhury
Independent visiting researcher
Principal supervisor :
Siva Reddy
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Luca Scimeca
Postdoctorate - Université de Montréal
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Dragos Secrieru
Collaborating Alumni - Université de Montréal
Marcin Sendera
Collaborating Alumni - Université de Montréal
Github
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Divya Sharma
Postdoctorate
Co-supervisor :
Alex Hernandez-Garcia
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Marco Stock
Independent visiting researcher - Technical University of Munich
Github
Mélisande Astrid Crystal Teng
PhD - Université de Montréal
Co-supervisor :
Hugo Larochelle
Website
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Ivan Titov
Independent visiting researcher
Principal supervisor :
Siva Reddy
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Alex Tong
Collaborating Alumni - Université de Montréal
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Github
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Donna Vakalis
Postdoctorate - Université de Montréal
Co-supervisor :
David Rolnick
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Siddarth Venkatraman
PhD - Université de Montréal
Principal supervisor :
Glen Berseth
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Omar G. Younis
Collaborating researcher
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Taeyoung YUN
Collaborating researcher - Université de Montréal
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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
Collaborating Alumni - McGill University
Principal supervisor :
Doina Precup
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Publications

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
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
Combining Spatial and Temporal Abstraction in Planning for Better Generalization
Mingde Zhao
Safa Alver
Harm van Seijen
Romain Laroche
Doina Precup
Yoshua Bengio
2023-01-01
arXiv.org (preprint)
doi.org
Conditional Flow Matching: Simulation-Free Dynamic Optimal Transport
Alexander Tong
Nikolay Malkin
Guillaume Huguet
Yanlei Zhang
Jarrid Rector-Brooks
Kilian Fatras
Guy Wolf
Yoshua Bengio
2023-01-01
arXiv.org (preprint)
doi.org
Conditional Flow Matching: Simulation-Free Dynamic Optimal Transport
Alexander Tong
Nikolay Malkin
Guillaume Huguet
Yanlei Zhang
Jarrid Rector-Brooks
Kilian Fatras
Guy Wolf
Yoshua Bengio
Continuous normalizing flows (CNFs) are an attractive generative modeling technique, but they have thus far been held back by limitations i… (see more)n their simulation-based maximum likelihood training. In this paper, we introduce a new technique called conditional flow matching (CFM), a simulation-free training objective for CNFs. CFM features a stable regression objective like that used to train the stochastic flow in diffusion models but enjoys the efficient inference of deterministic flow models. In contrast to both diffusion models and prior CNF training algorithms, our CFM objec-tive does not require the source distribution to be Gaussian or require evaluation of its density. Based on this new objective, we also introduce optimal transport CFM (OT-CFM), which creates simpler flows that are more stable to train and lead to faster inference, as evaluated in our experiments. Training CNFs with CFM improves results on a variety of conditional and unconditional generation tasks such as inferring single cell dynamics, unsupervised image translation, and Schr ¨ odinger bridge inference. Code is available at https://github.com/atong01/ conditional-flow-matching .
2023-01-01
arXiv.org (preprint)
doi.org