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
Dinghuai Zhang
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Aaron Courville
Site web
Github
Tianyu Zhang
Doctorat - UdeM
Site web
Github
Google Scholar
Nicole Zhang
Doctorat - McGill
Superviseur⋅e principal⋅e :
Mathieu Blanchette
Github
Google Scholar
Harry Zhao
Doctorat - McGill
Superviseur⋅e principal⋅e :
Doina Precup
Site web
Github
Google Scholar

Publications

Were RNNs All We Needed?
Leo Feng
Frederick Tung
Mohamed Osama Ahmed
Yoshua Bengio
Hossein Hajimirsadeghi
2024-10-02
ArXiv (prépublication)
doi.org
arxiv.org
Were RNNs All We Needed?
Leo Feng
Frederick Tung
Mohamed Osama Ahmed
Yoshua Bengio
Hossein Hajimirsadeghi
2024-10-02
ArXiv (prépublication)
doi.org
arxiv.org
Were RNNs All We Needed?
Leo Feng
Frederick Tung
Mohamed Osama Ahmed
Yoshua Bengio
Hossein Hajimirsadeghi
The introduction of Transformers in 2017 reshaped the landscape of deep learning. Originally proposed for sequence modelling, Transformers h… (voir plus)ave since achieved widespread success across various domains. However, the scalability limitations of Transformers - particularly with respect to sequence length - have sparked renewed interest in novel recurrent models that are parallelizable during training, offer comparable performance, and scale more effectively. In this work, we revisit sequence modelling from a historical perspective, focusing on Recurrent Neural Networks (RNNs), which dominated the field for two decades before the rise of Transformers. Specifically, we examine LSTMs (1997) and GRUs (2014). We demonstrate that by simplifying these models, we can derive minimal versions (minLSTMs and minGRUs) that (1) use fewer parameters than their traditional counterparts, (2) are fully parallelizable during training, and (3) achieve surprisingly competitive performance on a range of tasks, rivalling recent models including Transformers.
2024-10-02
ArXiv (prépublication)
doi.org
arxiv.org
Were RNNs All We Needed?
Leo Feng
Frederick Tung
Mohamed Osama Ahmed
Yoshua Bengio
Hossein Hajimirsadeghi
The introduction of Transformers in 2017 reshaped the landscape of deep learning. Originally proposed for sequence modelling, Transformers h… (voir plus)ave since achieved widespread success across various domains. However, the scalability limitations of Transformers - particularly with respect to sequence length - have sparked renewed interest in novel recurrent models that are parallelizable during training, offer comparable performance, and scale more effectively. In this work, we revisit sequence modelling from a historical perspective, focusing on Recurrent Neural Networks (RNNs), which dominated the field for two decades before the rise of Transformers. Specifically, we examine LSTMs (1997) and GRUs (2014). We demonstrate that by simplifying these models, we can derive minimal versions (minLSTMs and minGRUs) that (1) use fewer parameters than their traditional counterparts, (2) are fully parallelizable during training, and (3) achieve surprisingly competitive performance on a range of tasks, rivalling recent models including Transformers.
2024-10-02
ArXiv (prépublication)
doi.org
arxiv.org
Were RNNs All We Needed?
Leo Feng
Frederick Tung
Mohamed Osama Ahmed
Yoshua Bengio
Hossein Hajimirsadeghi
2024-10-02
ArXiv (prépublication)
doi.org
arxiv.org
Were RNNs All We Needed?
Leo Feng
Frederick Tung
Mohamed Osama Ahmed
Yoshua Bengio
Hossein Hajimirsadeghi
The introduction of Transformers in 2017 reshaped the landscape of deep learning. Originally proposed for sequence modelling, Transformers h… (voir plus)ave since achieved widespread success across various domains. However, the scalability limitations of Transformers - particularly with respect to sequence length - have sparked renewed interest in novel recurrent models that are parallelizable during training, offer comparable performance, and scale more effectively. In this work, we revisit sequence modelling from a historical perspective, focusing on Recurrent Neural Networks (RNNs), which dominated the field for two decades before the rise of Transformers. Specifically, we examine LSTMs (1997) and GRUs (2014). We demonstrate that by simplifying these models, we can derive minimal versions (minLSTMs and minGRUs) that (1) use fewer parameters than their traditional counterparts, (2) are fully parallelizable during training, and (3) achieve surprisingly competitive performance on a range of tasks, rivalling recent models including Transformers.
2024-10-02
ArXiv (prépublication)
doi.org
arxiv.org
Were RNNs All We Needed?
Leo Feng
Frederick Tung
Mohamed Osama Ahmed
Yoshua Bengio
Hossein Hajimirsadeghi
The introduction of Transformers in 2017 reshaped the landscape of deep learning. Originally proposed for sequence modelling, Transformers h… (voir plus)ave since achieved widespread success across various domains. However, the scalability limitations of Transformers - particularly with respect to sequence length - have sparked renewed interest in novel recurrent models that are parallelizable during training, offer comparable performance, and scale more effectively. In this work, we revisit sequence modelling from a historical perspective, focusing on Recurrent Neural Networks (RNNs), which dominated the field for two decades before the rise of Transformers. Specifically, we examine LSTMs (1997) and GRUs (2014). We demonstrate that by simplifying these models, we can derive minimal versions (minLSTMs and minGRUs) that (1) use fewer parameters than their traditional counterparts, (2) are fully parallelizable during training, and (3) achieve surprisingly competitive performance on a range of tasks, rivalling recent models including Transformers.
2024-10-02
ArXiv (prépublication)
doi.org
arxiv.org
Were RNNs All We Needed?
Leo Feng
Frederick Tung
Mohamed Osama Ahmed
Yoshua Bengio
Hossein Hajimirsadeghi
2024-10-02
ArXiv (prépublication)
doi.org
arxiv.org
Causal Discovery in Astrophysics: Unraveling Supermassive Black Hole and Galaxy Coevolution
Zehao Jin
Mario Pasquato
Benjamin L. Davis
Tristan Deleu
Yu Luo
Changhyun Cho
Pablo Lemos
Laurence Perreault-Levasseur
Yoshua Bengio
Xi 熙 Kang 康
Andrea Maccio
Yashar Hezaveh
Correlation does not imply causation, but patterns of statistical association between variables can be exploited to infer a causal structure… (voir plus) (even with purely observational data) with the burgeoning field of causal discovery. As a purely observational science, astrophysics has much to gain by exploiting these new methods. The supermassive black hole (SMBH)–galaxy interaction has long been constrained by observed scaling relations, which is low-scatter correlations between variables such as SMBH mass and the central velocity dispersion of stars in a host galaxy's bulge. This study, using advanced causal discovery techniques and an up-to-date data set, reveals a causal link between galaxy properties and dynamically measured SMBH masses. We apply a score-based Bayesian framework to compute the exact conditional probabilities of every causal structure that could possibly describe our galaxy sample. With the exact posterior distribution, we determine the most likely causal structures and notice a probable causal reversal when separating galaxies by morphology. In elliptical galaxies, bulge properties (built from major mergers) tend to influence SMBH growth, while, in spiral galaxies, SMBHs are seen to affect host galaxy properties, potentially through feedback in gas-rich environments. For spiral galaxies, SMBHs progressively quench star formation, whereas, in elliptical galaxies, quenching is complete, and the causal connection has reversed. Our findings support theoretical models of hierarchical assembly of galaxies and active galactic nuclei feedback regulating galaxy evolution. Our study suggests the potentiality for further exploration of causal links in astrophysical and cosmological scaling relations, as well as any other observational science.
2024-10-01
ArXiv (prépublication)
doi.org
arxiv.org
Causal Discovery in Astrophysics: Unraveling Supermassive Black Hole and Galaxy Coevolution
Zehao Jin
Mario Pasquato
Benjamin L. Davis
Tristan Deleu
Yu Luo
Changhyun Cho
Pablo Lemos
Laurence Perreault-Levasseur
Yoshua Bengio
Xi Kang
Andrea Maccio
Yashar Hezaveh
Correlation does not imply causation, but patterns of statistical association between variables can be exploited to infer a causal structure… (voir plus) (even with purely observational data) with the burgeoning field of causal discovery. As a purely observational science, astrophysics has much to gain by exploiting these new methods. The supermassive black hole (SMBH)--galaxy interaction has long been constrained by observed scaling relations, that is low-scatter correlations between variables such as SMBH mass and the central velocity dispersion of stars in a host galaxy's bulge. This study, using advanced causal discovery techniques and an up-to-date dataset, reveals a causal link between galaxy properties and dynamically-measured SMBH masses. We apply a score-based Bayesian framework to compute the exact conditional probabilities of every causal structure that could possibly describe our galaxy sample. With the exact posterior distribution, we determine the most likely causal structures and notice a probable causal reversal when separating galaxies by morphology. In elliptical galaxies, bulge properties (built from major mergers) tend to influence SMBH growth, while in spiral galaxies, SMBHs are seen to affect host galaxy properties, potentially through feedback in gas-rich environments. For spiral galaxies, SMBHs progressively quench star formation, whereas in elliptical galaxies, quenching is complete, and the causal connection has reversed. Our findings support theoretical models of hierarchical assembly of galaxies and active galactic nuclei feedback regulating galaxy evolution. Our study suggests the potentiality for further exploration of causal links in astrophysical and cosmological scaling relations, as well as any other observational science.
2024-10-01
ArXiv (prépublication)
doi.org
arxiv.org
Causal Discovery in Astrophysics: Unraveling Supermassive Black Hole and Galaxy Coevolution
Zehao Jin
Mario Pasquato
Benjamin L. Davis
Tristan Deleu
Yu Luo
Changhyun Cho
Pablo Lemos
Laurence Perreault-Levasseur
Yoshua Bengio
Xi 熙 Kang 康
Andrea Maccio
Yashar Hezaveh
Correlation does not imply causation, but patterns of statistical association between variables can be exploited to infer a causal structure… (voir plus) (even with purely observational data) with the burgeoning field of causal discovery. As a purely observational science, astrophysics has much to gain by exploiting these new methods. The supermassive black hole (SMBH)–galaxy interaction has long been constrained by observed scaling relations, which is low-scatter correlations between variables such as SMBH mass and the central velocity dispersion of stars in a host galaxy's bulge. This study, using advanced causal discovery techniques and an up-to-date data set, reveals a causal link between galaxy properties and dynamically measured SMBH masses. We apply a score-based Bayesian framework to compute the exact conditional probabilities of every causal structure that could possibly describe our galaxy sample. With the exact posterior distribution, we determine the most likely causal structures and notice a probable causal reversal when separating galaxies by morphology. In elliptical galaxies, bulge properties (built from major mergers) tend to influence SMBH growth, while, in spiral galaxies, SMBHs are seen to affect host galaxy properties, potentially through feedback in gas-rich environments. For spiral galaxies, SMBHs progressively quench star formation, whereas, in elliptical galaxies, quenching is complete, and the causal connection has reversed. Our findings support theoretical models of hierarchical assembly of galaxies and active galactic nuclei feedback regulating galaxy evolution. Our study suggests the potentiality for further exploration of causal links in astrophysical and cosmological scaling relations, as well as any other observational science.
2024-10-01
ArXiv (prépublication)
doi.org
arxiv.org
MAP: Model Merging with Amortized Pareto Front Using Limited Computation
Lu Liu
Tianyu Zhang
Zhiqi Bu
Suyuchen Wang
Huan He
Jie Fu
Yonghui Wu
Jiang Bian
Yong Chen
Yoshua Bengio
2024-10-01
NeurIPS.cc/2024/Workshop/Federated_Learning (présentation orale)
openreview.net
openreview.net