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
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 Cassidy MacNeil, adjointe principale et responsable des opérations cassidy.macneil@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 de recherche - Cambridge University
Superviseur⋅e principal⋅e :
Collaborateur·rice de recherche - N/A
Superviseur⋅e principal⋅e :
Collaborateur·rice de recherche - KAIST
Collaborateur·rice alumni - UdeM
Postdoctorat - UdeM
Superviseur⋅e principal⋅e :
Collaborateur·rice de recherche - UdeM
Stagiaire de recherche - KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY
Collaborateur·rice de recherche - s.o.
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Visiteur de recherche indépendant - UdeM
Collaborateur·rice de recherche - Ying Wu Coll of Computing
Collaborateur·rice de recherche - University of Waterloo
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Doctorat - UdeM
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Collaborateur·rice alumni - UdeM
Co-superviseur⋅e :
Collaborateur·rice de recherche - University of Cambridge
Collaborateur·rice alumni - UdeM
Co-superviseur⋅e :
Collaborateur·rice de recherche - UdeM
Doctorat - McGill
Superviseur⋅e principal⋅e :

Publications

Assessing SAM for Tree Crown Instance Segmentation from Drone Imagery
Extendable Planning via Multiscale Diffusion
Chang Chen
Hany Hamed
Doojin Baek
Taegu Kang
Long-horizon planning is crucial in complex environments, but diffusion-based planners like Diffuser are limited by the trajectory lengths o… (voir plus)bserved during training. This creates a dilemma: long trajectories are needed for effective planning, yet they degrade model performance. In this paper, we introduce this extendable long-horizon planning challenge and propose a two-phase solution. First, Progressive Trajectory Extension incrementally constructs longer trajectories through multi-round compositional stitching. Second, the Hierarchical Multiscale Diffuser enables efficient training and inference over long horizons by reasoning across temporal scales. To avoid the need for multiple separate models, we propose Adaptive Plan Pondering and the Recursive HM-Diffuser, which unify hierarchical planning within a single model. Experiments show our approach yields strong performance gains, advancing scalable and efficient decision-making over long-horizons.
A scalable gene network model of regulatory dynamics in single cells
Joseph D Viviano
Alejandro Tejada-Lapuerta
Weixu Wang
Fabian J. Theis
Offline Model-Based Optimization: Comprehensive Review
Jiayao Gu
Zixuan Liu
Can Chen
What makes a theory of consciousness unscientific?
IIT-Concerned
Derek H. Arnold
Mark G. Baxter
Tristan A. Bekinschtein
James W. Bisley
Jacob Browning
Dean V. Buonomano
David Carmel
Marisa Carrasco
Peter Carruthers
Olivia Carter
Dorita H. F. Chang
Mouslim Cherkaoui
Axel Cleeremans
Michael A. Cohen
Philip R. Corlett
Kalina Christoff
Sam Cumming … (voir 80 de plus)
Cody A. Cushing
Beatrice de Gelder
Felipe De Brigard
Daniel C. Dennett
Nadine Dijkstra
Adrien Doerig
Paul E. Dux
Stephen M. Fleming
Keith Frankish
Chris Frith
Sarah Garfinkel
Melvyn A. Goodale
Jacqueline Gottlieb
Jake R. Hanson
Ran R. Hassin
Michael H. Herzog
Cecilia Heyes
Po‐Jang Hsieh
Shao‐Min Hung
Robert W. Kentridge
Tomas Knapen
Nikos Konstantinou
Konrad P. Kording
Timo L. Kvamme
Sze Chai Kwok
Renzo C. Lanfranco
Hakwan Lau
Joseph E. LeDoux
Alan Lee
Camilo Libedinsky
Matthew D. Lieberman
Ying-Tung Lin
Kayuet Liu
Maro G. Machizawa
Julio Martínez-Trujillo
Janet Metcalfe
Matthias Michel
Kenneth D. Miller
Partha P. Mitra
Dean Mobbs
Robert M. Mok
Jorge Morales
Myrto Mylopoulos
Brian Odegaard
Charles C.-F. Or
Adrian M. Owen
David Pereplyotchik
Franco Pestilli
Megan A. K. Peters
Ian Phillips
Rosanne L. Rademaker
Dobromir Rahnev
Geraint Rees
Dario L. Ringach
Adina L. Roskies
Daniela Schiller
Aaron Schurger
D. Samuel Schwarzkopf
R. B. Y. Scott
Aaron R. Seitz
Joshua Shepherd
Juha Silvanto
Heleen A. Slagter
Barry Smith
Guillermo Solovey
David Soto
Hugo J. Spiers
Timo Stein
Vincent Taschereau‐Dumouchel
Frank Tong
Peter U. Tse
Jonas Vibell
Sebastian Watzl
Taylor W. Webb
Josh Weisberg
Thalia Wheatley
Michał Wierzchoń
Martijn E. Wokke
Karen Yan
Michał Klincewicz
Mitigating Shortcut Learning with Diffusion Counterfactuals and Diverse Ensembles
Alexander Rubinstein
Damien Teney
Seong Joon Oh
Armand Mihai Nicolicioiu
Spurious correlations in the data, where multiple cues are predictive of the target labels, often lead to a phenomenon known as shortcut lea… (voir plus)rning, where a model relies on erroneous, easy-to-learn cues while ignoring reliable ones. In this work, we propose
Shaping Inductive Bias in Diffusion Models through Frequency-Based Noise Control
Berton Earnshaw
Jason Hartford
Diffusion Probabilistic Models (DPMs) are powerful generative models that have achieved unparalleled success in a number of generative tasks… (voir plus). In this work, we aim to build inductive biases into the training and sampling of diffusion models to better accommodate the target distribution of the data to model. For topologically structured data, we devise a frequency-based noising operator to purposefully manipulate, and set, these inductive biases. We first show that appropriate manipulations of the noising forward process can lead DPMs to focus on particular aspects of the distribution to learn. We show that different datasets necessitate different inductive biases, and that appropriate frequency-based noise control induces increased generative performance compared to standard diffusion. Finally, we demonstrate the possibility of ignoring information at particular frequencies while learning. We show this in an image corruption and recovery task, where we train a DPM to recover the original target distribution after severe noise corruption.
Solving Bayesian Inverse Problems with Diffusion Priors and Off-Policy RL
This paper presents a practical application of Relative Trajectory Balance (RTB), a recently introduced off-policy reinforcement learning (R… (voir plus)L) objective that can asymptotically solve Bayesian inverse problems optimally. We extend the original work by using RTB to train conditional diffusion model posteriors from pretrained unconditional priors for challenging linear and non-linear inverse problems in vision, and science. We use the objective alongside techniques such as off-policy backtracking exploration to improve training. Importantly, our results show that existing training-free diffusion posterior methods struggle to perform effective posterior inference in latent space due to inherent biases.
Learning Decision Trees as Amortized Structure Inference
EarthView: A Large Scale Remote Sensing Dataset for Self-Supervision
Diego Velazquez
Pau Rodríguez
Sergio Alonso
Josep M. Gonfaus
Jordi Gonzalez
Gerardo Richarte
Javier Marin
Alexandre Lacoste
This paper presents EarthView, a comprehensive dataset specifically designed for self-supervision on remote sensing data, intended to enhanc… (voir plus)e deep learning applications on Earth monitoring tasks. The dataset spans 15 tera pixels of global remote-sensing data, combining imagery from a diverse range of sources, including NEON, Sentinel, and a novel release of 1m spatial resolution data from Satellogic. Our dataset provides a wide spectrum of image data with varying resolutions, harnessed from different sensors and organized coherently into an accessible HuggingFace dataset in parquet format. This data spans five years, from 2017 to 2022. Accompanying the dataset, we introduce EarthMAE, a tailored Masked Autoencoder, developed to tackle the distinct challenges of remote sensing data. Trained in a self-supervised fashion, EarthMAE effectively processes different data modalities such as hyperspectral, multispectral, topographical data, segmentation maps, and temporal structure. This model helps us show that pre-training on Satellogic data improves performance on downstream tasks. While there is still a gap to fill in MAE for heterogeneous data, we regard this innovative combination of an expansive, diverse dataset and a versatile model adapted for self-supervised learning as a stride forward in deep learning for Earth monitoring.
A physics-based data-driven model for CO$_2$ gas diffusion electrodes to drive automated laboratories
Abhishek Soni
Karry Ocean
Kevan Dettelbach
Ribwar Ahmadi
Mehrdad Mokhtari
Curtis P. Berlinguette
The electrochemical reduction of atmospheric CO…
In-Context Parametric Inference: Point or Distribution Estimators?
Bayesian and frequentist inference are two fundamental paradigms in statistical estimation. Bayesian methods treat hypotheses as random vari… (voir plus)ables, incorporating priors and updating beliefs via Bayes' theorem, whereas frequentist methods assume fixed but unknown hypotheses, relying on estimators like maximum likelihood. While extensive research has compared these approaches, the frequentist paradigm of obtaining point estimates has become predominant in deep learning, as Bayesian inference is challenging due to the computational complexity and the approximation gap of posterior estimation methods. However, a good understanding of trade-offs between the two approaches is lacking in the regime of amortized estimators, where in-context learners are trained to estimate either point values via maximum likelihood or maximum a posteriori estimation, or full posteriors using normalizing flows, score-based diffusion samplers, or diagonal Gaussian approximations, conditioned on observations. To help resolve this, we conduct a rigorous comparative analysis spanning diverse problem settings, from linear models to shallow neural networks, with a robust evaluation framework assessing both in-distribution and out-of-distribution generalization on tractable tasks. Our experiments indicate that amortized point estimators generally outperform posterior inference, though the latter remain competitive in some low-dimensional problems, and we further discuss why this might be the case.