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
Stefan Bauer
Visiteur de recherche indépendant
Co-superviseur⋅e :
Guillaume Lajoie
Google Scholar
Paul Bertin
Doctorat - UdeM
Joyce Chai
Visiteur de recherche indépendant
Superviseur⋅e principal⋅e :
Siva Reddy
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
Collaborateur·rice alumni - UdeM
Site web
Github
Google Scholar
Aniket Didolkar
Doctorat - UdeM
Site web
Github
Google Scholar
Abdessamad EL KABID
Collaborateur·rice alumni - UdeM
Co-superviseur⋅e :
Loubna Benabbou
Desmond Elliott
Visiteur de recherche indépendant
Superviseur⋅e principal⋅e :
Siva Reddy
Site web
Google Scholar
Eric Elmoznino
Doctorat - UdeM
Co-superviseur⋅e :
Guillaume Lajoie
Site web
Github
Google Scholar
Léna Ezzine
Doctorat - UdeM
Jean-Pierre Falet
Doctorat - UdeM
Site web
Github
Google Scholar
Leo Feng
Doctorat - UdeM
Site web
Google Scholar
Pietro Greiner
Doctorat
Google Scholar
Mohsin Hasan
Doctorat - UdeM
Site web
Github
Google Scholar
Edward Hu
Doctorat - UdeM
Moksh Jain
Doctorat - UdeM
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-Garcia
Salem Lahlou
Collaborateur·rice alumni - UdeM
Tabitha Edith Lee
Postdoctorat - UdeM
Superviseur⋅e principal⋅e :
Glen Berseth
Site web
Github
Google Scholar
Chenghao Liu
Collaborateur·rice alumni
Site web
Github
Google Scholar
Zhen Liu
Collaborateur·rice alumni - UdeM
Superviseur⋅e principal⋅e :
Liam Paull
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
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
Collaborateur·rice de recherche - University of Waterloo
Superviseur⋅e principal⋅e :
David Rolnick
Google Scholar
Nassim Rahaman
Collaborateur·rice alumni - Max-Planck-Institute for Intelligent Systems
Amine RAZIG
Collaborateur·rice 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
Google Scholar
Camille Rochefort-Boulanger
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Julie Hussin
Github
Abhik Roychoudhury Roychoudhury
Visiteur de recherche indépendant
Superviseur⋅e principal⋅e :
Siva Reddy
Github
Luca Scimeca
Postdoctorat - UdeM
Site web
Github
Google Scholar
Dragos Secrieru
Collaborateur·rice alumni - UdeM
Marcin Sendera
Collaborateur·rice alumni - UdeM
Github
Google Scholar
Divya Sharma
Postdoctorat
Co-superviseur⋅e :
Alex Hernandez-Garcia
Site web
Github
Marco Stock
Visiteur de recherche indépendant - Technical University of Munich
Github
Mélisande Astrid Crystal Teng
Doctorat - UdeM
Co-superviseur⋅e :
Hugo Larochelle
Site web
Github
Ivan Titov
Visiteur de recherche indépendant
Superviseur⋅e principal⋅e :
Siva Reddy
Site web
Google Scholar
Alex Tong
Collaborateur·rice alumni - UdeM
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
Omar G. Younis
Collaborateur·rice de recherche
Site web
Github
Google Scholar
Taeyoung YUN
Collaborateur·rice de recherche - UdeM
Github
Tianyu Zhang
Doctorat - UdeM
Site web
Github
Google Scholar
Nicole Zhang
Doctorat - McGill
Superviseur⋅e principal⋅e :
Mathieu Blanchette
Site web
Github
Google Scholar
Dinghuai Zhang
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Aaron Courville
Site web
Harry Zhao
Collaborateur·rice alumni - 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
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
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
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
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
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