Portrait de Yoshua Bengio

Yoshua Bengio

Membre académique principal
Chaire en IA Canada-CIFAR
Professeur titulaire, Université de Montréal, Département d'informatique et de recherche opérationnelle
Fondateur et Conseiller scientifique, Équipe de direction
Sujets de recherche
Apprentissage automatique médical
Apprentissage de représentations
Apprentissage par renforcement
Apprentissage profond
Causalité
Modèles génératifs
Modèles probabilistes
Modélisation moléculaire
Neurosciences computationnelles
Raisonnement
Réseaux de neurones en graphes
Réseaux de neurones récurrents
Théorie de l'apprentissage automatique
Traitement du langage naturel
Site web
Google Scholar

Biographie

*Pour toute demande média, veuillez écrire à medias@mila.quebec.

Pour plus d’information, contactez Marie-Josée Beauchamp, adjointe administrative à marie-josee.beauchamp@mila.quebec.

Reconnu comme une sommité mondiale en intelligence artificielle, Yoshua Bengio s’est surtout distingué par son rôle de pionnier en apprentissage profond, ce qui lui a valu le prix A. M. Turing 2018, le « prix Nobel de l’informatique », avec Geoffrey Hinton et Yann LeCun. Il est professeur titulaire à l’Université de Montréal, fondateur et conseiller scientifique de Mila – Institut québécois d’intelligence artificielle, et codirige en tant que senior fellow le programme Apprentissage automatique, apprentissage biologique de l'Institut canadien de recherches avancées (CIFAR). Il occupe également la fonction de conseiller spécial et directeur scientifique fondateur d’IVADO.

En 2018, il a été l’informaticien qui a recueilli le plus grand nombre de nouvelles citations au monde. En 2019, il s’est vu décerner le prestigieux prix Killam. Depuis 2022, il détient le plus grand facteur d’impact (h-index) en informatique à l’échelle mondiale. Il est fellow de la Royal Society de Londres et de la Société royale du Canada, et officier de l’Ordre du Canada.

Soucieux des répercussions sociales de l’IA et de l’objectif que l’IA bénéficie à tous, il a contribué activement à la Déclaration de Montréal pour un développement responsable de l’intelligence artificielle.

Étudiants actuels

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

Publications

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… (voir plus) 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 (prépublication)
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… (voir plus) 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 (prépublication)
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… (voir plus) 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 (prépublication)
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… (voir plus) 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 (prépublication)
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… (voir plus) 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 (prépublication)
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
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 (prépublication)
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 (prépublication)
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… (voir plus)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 (prépublication)
doi.org
Constant Memory Attentive Neural Processes
Leo Feng
Frederick Tung
Hossein Hajimirsadeghi
Yoshua Bengio
Mohamed Osama Ahmed
2023-01-01
arXiv.org (prépublication)
doi.org
DynGFN: Bayesian Dynamic Causal Discovery using Generative Flow Networks
Lazar Atanackovic
Alexander Tong
Jason Hartford
Leo Jingyu Lee
Bo Wang
Yoshua Bengio
Learning the causal structure of observable variables is a central focus for scientific discovery. Bayesian causal discovery methods tackle… (voir plus) this problem by learning a posterior over the set of admissible graphs given our priors and observations. Existing methods primarily consider observations from static systems and assume the underlying causal structure takes the form of a directed acyclic graph (DAG). In settings with dynamic feedback mechanisms that regulate the trajectories of individual variables, this acyclicity assumption fails unless we account for time. We focus on learning Bayesian posteriors over cyclic graphs and treat causal discovery as a problem of sparse identification of a dynamical sys-tem. This imposes a natural temporal causal order between variables and captures cyclic feedback loops through time. Under this lens, we propose a new framework for Bayesian causal discovery for dynamical systems and present a novel generative flow network architecture (DynGFN) tailored for this task. Our results indicate that DynGFN learns posteriors that better encapsulate the distributions over admissible cyclic causal structures compared to counterpart state-of-the-art approaches.
2023-01-01
arXiv.org (prépublication)
doi.org
GFlowNet Foundations
Yoshua Bengio
Salem Lahlou
Tristan Deleu
Edward J. Hu
Mo Tiwari
Emmanuel Bengio