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

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

Collaborateur·rice alumni - McGill
Collaborateur·rice alumni - UdeM
Collaborateur·rice de recherche - Cambridge University
Superviseur⋅e principal⋅e :
Doctorat - UdeM
Visiteur de recherche indépendant
Co-superviseur⋅e :
Doctorat - UdeM
Visiteur de recherche indépendant
Superviseur⋅e principal⋅e :
Collaborateur·rice de recherche - N/A
Superviseur⋅e principal⋅e :
Doctorat - UdeM
Collaborateur·rice de recherche - KAIST
Collaborateur·rice alumni - UdeM
Collaborateur·rice alumni - UdeM
Co-superviseur⋅e :
Visiteur de recherche indépendant
Superviseur⋅e principal⋅e :
Doctorat - UdeM
Co-superviseur⋅e :
Doctorat - UdeM
Doctorat - UdeM
Doctorat - UdeM
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Collaborateur·rice alumni - UdeM
Postdoctorat - UdeM
Superviseur⋅e principal⋅e :
Collaborateur·rice alumni - UdeM
Postdoctorat - UdeM
Superviseur⋅e principal⋅e :
Collaborateur·rice alumni - UdeM
Superviseur⋅e principal⋅e :
Collaborateur·rice alumni
Doctorat - UdeM
Collaborateur·rice alumni - UdeM
Doctorat - UdeM
Co-superviseur⋅e :
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Postdoctorat - UdeM
Superviseur⋅e principal⋅e :
Visiteur de recherche indépendant - UdeM
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Collaborateur·rice de recherche - Ying Wu Coll of Computing
Collaborateur·rice de recherche - University of Waterloo
Superviseur⋅e principal⋅e :
Collaborateur·rice alumni - Max-Planck-Institute for Intelligent Systems
Collaborateur·rice de recherche - UdeM
Co-superviseur⋅e :
Doctorat - UdeM
Postdoctorat - UdeM
Visiteur de recherche indépendant - UdeM
Postdoctorat - UdeM
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Visiteur de recherche indépendant
Superviseur⋅e principal⋅e :
Collaborateur·rice alumni - UdeM
Collaborateur·rice alumni - UdeM
Postdoctorat
Co-superviseur⋅e :
Visiteur de recherche indépendant - Technical University of Munich
Doctorat - UdeM
Co-superviseur⋅e :
Visiteur de recherche indépendant
Superviseur⋅e principal⋅e :
Collaborateur·rice alumni - UdeM
Postdoctorat - UdeM
Co-superviseur⋅e :
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Collaborateur·rice de recherche
Collaborateur·rice de recherche - UdeM
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Doctorat - McGill
Superviseur⋅e principal⋅e :
Collaborateur·rice alumni - McGill
Superviseur⋅e principal⋅e :

Publications

Leveraging Diffusion Disentangled Representations to Mitigate Shortcuts in Underspecified Visual Tasks
Alexander Rubinstein
Armand Mihai Nicolicioiu
Damien Teney
Spurious correlations in the data, where multiple cues are predictive of the target labels, often lead to shortcut learning phenomena, where… (voir plus) a model may rely on erroneous, easy-to-learn, cues while ignoring reliable ones. In this work, we propose an ensemble diversification framework exploiting the generation of synthetic counterfactuals using Diffusion Probabilistic Models (DPMs). We discover that DPMs have the inherent capability to represent multiple visual cues independently, even when they are largely correlated in the training data. We leverage this characteristic to encourage model diversity and empirically show the efficacy of the approach with respect to several diversification objectives. We show that diffusion-guided diversification can lead models to avert attention from shortcut cues, achieving ensemble diversity performance comparable to previous methods requiring additional data collection.
AI and Catastrophic Risk
Abstract:Since OpenAI's release of the very large language models Chat-GPT and GPT-4, the potential dangers of AI have garnered widespread p… (voir plus)ublic attention. In this essay, the author reviews the threats to democracy posed by the possibility of "rogue AIs," dangerous and powerful AIs that would execute harmful goals, irrespective of whether the outcomes are intended by humans. To mitigate against the risk that rogue AIs present to democracy and geopolitical stability, the author argues that research into safe and defensive AIs should be conducted by a multilateral, international network of research laboratories.
AI and Catastrophic Risk
AI and Catastrophic Risk
Abstract:Since OpenAI's release of the very large language models Chat-GPT and GPT-4, the potential dangers of AI have garnered widespread p… (voir plus)ublic attention. In this essay, the author reviews the threats to democracy posed by the possibility of "rogue AIs," dangerous and powerful AIs that would execute harmful goals, irrespective of whether the outcomes are intended by humans. To mitigate against the risk that rogue AIs present to democracy and geopolitical stability, the author argues that research into safe and defensive AIs should be conducted by a multilateral, international network of research laboratories.
AI and Catastrophic Risk
AI and Catastrophic Risk
Abstract:Since OpenAI's release of the very large language models Chat-GPT and GPT-4, the potential dangers of AI have garnered widespread p… (voir plus)ublic attention. In this essay, the author reviews the threats to democracy posed by the possibility of "rogue AIs," dangerous and powerful AIs that would execute harmful goals, irrespective of whether the outcomes are intended by humans. To mitigate against the risk that rogue AIs present to democracy and geopolitical stability, the author argues that research into safe and defensive AIs should be conducted by a multilateral, international network of research laboratories.
AI and Catastrophic Risk
AI and Catastrophic Risk
Tree Cross Attention
Frederick Tung
Hossein Hajimirsadeghi
Mohamed Osama Ahmed
Tree Cross Attention
Frederick Tung
Hossein Hajimirsadeghi
Mohamed Osama Ahmed
RECOVER identifies synergistic drug combinations in vitro through sequential model optimization
Thomas Gaudelet
Andrew Anighoro
Torsten Gross
Francisco Martínez-Peña
Eileen L. Tang
M.S. Suraj
Cristian Regep
Jeremy B.R. Hayter
Nicholas Valiante
Mike Tyers
Charles E.S. Roberts
Michael M. Bronstein
Luke L. Lairson
Jake P. Taylor-King
GEO-Bench: Toward Foundation Models for Earth Monitoring
Alexandre Lacoste
Nils Lehmann
Pau Rodriguez
Evan David Sherwin
Hannah Kerner
Björn Lütjens
Jeremy Andrew Irvin
David Dao
Hamed Alemohammad
Mehmet Gunturkun
David Vazquez
Dava Newman
Stefano Ermon
Xiao Xiang Zhu
Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to subst… (voir plus)antial increases in generalization to downstream tasks. Such models, recently coined foundation models, have been transformational to the field of natural language processing. Variants have also been proposed for image data, but their applicability to remote sensing tasks is limited. To stimulate the development of foundation models for Earth monitoring, we propose a benchmark comprised of six classification and six segmentation tasks, which were carefully curated and adapted to be both relevant to the field and well-suited for model evaluation. We accompany this benchmark with a robust methodology for evaluating models and reporting aggregated results to enable a reliable assessment of progress. Finally, we report results for 20 baselines to gain information about the performance of existing models. We believe that this benchmark will be a driver of progress across a variety of Earth monitoring tasks.