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

Publications

Thompson Sampling for Improved Exploration in GFlowNets
Jarrid Rector-Brooks
Kanika Madan
Moksh J. Jain
Maksym Korablyov
Cheng-Hao Liu
Nikolay Malkin
Generative flow networks (GFlowNets) are amortized variational inference algorithms that treat sampling from a distribution over composition… (voir plus)al objects as a sequential decision-making problem with a learnable action policy. Unlike other algorithms for hierarchical sampling that optimize a variational bound, GFlowNet algorithms can stably run off-policy, which can be advantageous for discovering modes of the target distribution. Despite this flexibility in the choice of behaviour policy, the optimal way of efficiently selecting trajectories for training has not yet been systematically explored. In this paper, we view the choice of trajectories for training as an active learning problem and approach it using Bayesian techniques inspired by methods for multi-armed bandits. The proposed algorithm, Thompson sampling GFlowNets (TS-GFN), maintains an approximate posterior distribution over policies and samples trajectories from this posterior for training. We show in two domains that TS-GFN yields improved exploration and thus faster convergence to the target distribution than the off-policy exploration strategies used in past work.
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
Gabriel Huang
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.
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
Gabriel Huang
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.
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
Gabriel Huang
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.
Cycle Consistency Driven Object Discovery
Aniket Rajiv Didolkar
Anirudh Goyal
Developing deep learning models that effectively learn object-centric representations, akin to human cognition, remains a challenging task. … (voir plus)Existing approaches facilitate object discovery by representing objects as fixed-size vectors, called ``slots'' or ``object files''. While these approaches have shown promise in certain scenarios, they still exhibit certain limitations. First, they rely on architectural priors which can be unreliable and usually require meticulous engineering to identify the correct objects. Second, there has been a notable gap in investigating the practical utility of these representations in downstream tasks. To address the first limitation, we introduce a method that explicitly optimizes the constraint that each object in a scene should be associated with a distinct slot. We formalize this constraint by introducing consistency objectives which are cyclic in nature. By integrating these consistency objectives into various existing slot-based object-centric methods, we showcase substantial improvements in object-discovery performance. These enhancements consistently hold true across both synthetic and real-world scenes, underscoring the effectiveness and adaptability of the proposed approach. To tackle the second limitation, we apply the learned object-centric representations from the proposed method to two downstream reinforcement learning tasks, demonstrating considerable performance enhancements compared to conventional slot-based and monolithic representation learning methods. Our results suggest that the proposed approach not only improves object discovery, but also provides richer features for downstream tasks.
Cycle Consistency Driven Object Discovery
Aniket Rajiv Didolkar
Anirudh Goyal
Developing deep learning models that effectively learn object-centric representations, akin to human cognition, remains a challenging task. … (voir plus)Existing approaches facilitate object discovery by representing objects as fixed-size vectors, called ``slots'' or ``object files''. While these approaches have shown promise in certain scenarios, they still exhibit certain limitations. First, they rely on architectural priors which can be unreliable and usually require meticulous engineering to identify the correct objects. Second, there has been a notable gap in investigating the practical utility of these representations in downstream tasks. To address the first limitation, we introduce a method that explicitly optimizes the constraint that each object in a scene should be associated with a distinct slot. We formalize this constraint by introducing consistency objectives which are cyclic in nature. By integrating these consistency objectives into various existing slot-based object-centric methods, we showcase substantial improvements in object-discovery performance. These enhancements consistently hold true across both synthetic and real-world scenes, underscoring the effectiveness and adaptability of the proposed approach. To tackle the second limitation, we apply the learned object-centric representations from the proposed method to two downstream reinforcement learning tasks, demonstrating considerable performance enhancements compared to conventional slot-based and monolithic representation learning methods. Our results suggest that the proposed approach not only improves object discovery, but also provides richer features for downstream tasks.
Spotlight Attention: Robust Object-Centric Learning With a Spatial Locality Prior
Ayush K Chakravarthy
Trang M. Nguyen
Anirudh Goyal
Michael Curtis Mozer
The aim of object-centric vision is to construct an explicit representation of the objects in a scene. This representation is obtained via a… (voir plus) set of interchangeable modules called \emph{slots} or \emph{object files} that compete for local patches of an image. The competition has a weak inductive bias to preserve spatial continuity; consequently, one slot may claim patches scattered diffusely throughout the image. In contrast, the inductive bias of human vision is strong, to the degree that attention has classically been described with a spotlight metaphor. We incorporate a spatial-locality prior into state-of-the-art object-centric vision models and obtain significant improvements in segmenting objects in both synthetic and real-world datasets. Similar to human visual attention, the combination of image content and spatial constraints yield robust unsupervised object-centric learning, including less sensitivity to model hyperparameters.
Spotlight Attention: Robust Object-Centric Learning With a Spatial Locality Prior
Ayush K Chakravarthy
Trang M. Nguyen
Anirudh Goyal
Michael Curtis Mozer
Attention Schema in Neural Agents
Dianbo Liu
Samuele Bolotta
Mike He Zhu
Attention has become a common ingredient in deep learning architectures. It adds a dynamical selection of information on top of the static s… (voir plus)election of information supported by weights. In the same way, we can imagine a higher-order informational filter built on top of attention: an Attention Schema (AS), namely, a descriptive and predictive model of attention. In cognitive neuroscience, Attention Schema Theory (AST) supports this idea of distinguishing attention from AS. A strong prediction of this theory is that an agent can use its own AS to also infer the states of other agents' attention and consequently enhance coordination with other agents. As such, multi-agent reinforcement learning would be an ideal setting to experimentally test the validity of AST. We explore different ways in which attention and AS interact with each other. Our preliminary results indicate that agents that implement the AS as a recurrent internal control achieve the best performance. In general, these exploratory experiments suggest that equipping artificial agents with a model of attention can enhance their social intelligence.
Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets
Dinghuai Zhang
Hanjun Dai
Nikolay Malkin
Ling Pan
Combinatorial optimization (CO) problems are often NP-hard and thus out of reach for exact algorithms, making them a tempting domain to appl… (voir plus)y machine learning methods. The highly structured constraints in these problems can hinder either optimization or sampling directly in the solution space. On the other hand, GFlowNets have recently emerged as a powerful machinery to efficiently sample from composite unnormalized densities sequentially and have the potential to amortize such solution-searching processes in CO, as well as generate diverse solution candidates. In this paper, we design Markov decision processes (MDPs) for different combinatorial problems and propose to train conditional GFlowNets to sample from the solution space. Efficient training techniques are also developed to benefit long-range credit assignment. Through extensive experiments on a variety of different CO tasks with synthetic and realistic data, we demonstrate that GFlowNet policies can efficiently find high-quality solutions. Our implementation is open-sourced at https://github.com/zdhNarsil/GFlowNet-CombOpt.
Model evaluation for extreme risks
Toby Shevlane
Sebastian Farquhar
Ben Garfinkel
Mary Phuong
Jess Whittlestone
Jade Leung
Daniel Kokotajlo
Nahema A. Marchal
Markus Anderljung
Noam Kolt
Lewis Ho
Divya Siddarth
Shahar Avin
W. Hawkins
Been Kim
Iason Gabriel
Vijay Bolina
Jack Clark
Paul F. Christiano … (voir 1 de plus)
Allan Dafoe
Model evaluation for extreme risks
Toby Shevlane
Sebastian Farquhar
Ben Garfinkel
Mary Phuong
Jess Whittlestone
Jade Leung
Daniel Kokotajlo
Nahema A. Marchal
Markus Anderljung
Noam Kolt
Lewis Ho
Divya Siddarth
Shahar Avin
W. Hawkins
Been Kim
Iason Gabriel
Vijay Bolina
Jack Clark
Paul F. Christiano … (voir 1 de plus)
Allan Dafoe
Current approaches to building general-purpose AI systems tend to produce systems with both beneficial and harmful capabilities. Further pro… (voir plus)gress in AI development could lead to capabilities that pose extreme risks, such as offensive cyber capabilities or strong manipulation skills. We explain why model evaluation is critical for addressing extreme risks. Developers must be able to identify dangerous capabilities (through"dangerous capability evaluations") and the propensity of models to apply their capabilities for harm (through"alignment evaluations"). These evaluations will become critical for keeping policymakers and other stakeholders informed, and for making responsible decisions about model training, deployment, and security.