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
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Collaborateur·rice de recherche - N/A
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Collaborateur·rice de recherche - KAIST
Visiteur de recherche indépendant
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Collaborateur·rice alumni - UdeM
Postdoctorat - UdeM
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Collaborateur·rice de recherche - UdeM
Postdoctorat - UdeM
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Stagiaire de recherche - KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY
Collaborateur·rice de recherche - s.o.
Doctorat - UdeM
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Visiteur de recherche indépendant - UdeM
Doctorat - UdeM
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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
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Collaborateur·rice de recherche - University of Cambridge
Collaborateur·rice alumni - UdeM
Co-superviseur⋅e :
Collaborateur·rice de recherche - UdeM
Doctorat - McGill
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Collaborateur·rice alumni - McGill
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Publications

Adsorption energies are necessary but not sufficient to identify good catalysts
Alexander Davis
Alexandre AGM Duval
Oleksandr Voznyy
Alex Hern'andez-Garcia
Leveraging a Fully Differentiable Integrated Assessment Model for RL and Inference
Koen Ponse
Kai-Hendrik Cohrs
Phillip Wozny
Andrew Robert Williams
Erman Acar
Aske Plaat
Thomas M. Moerland
Pierre Gentine
Gustau Camps-Valls
A HOT Dataset: 150,000 Buildings for HVAC Operations Transfer Research
A HOT Dataset: 150,000 Buildings for HVAC Operations Transfer Research
About 12% of global energy consumption is attributable to heating, ventilation, and air conditioning (HVAC) systems in buildings [11]. Machi… (voir plus)ne learning-based intelligent HVAC control offers significant energy efficiency potential, but progress is constrained by limited data for training and evaluating performance across different kinds of buildings. Existing datasets primarily target energy prediction rather than control applications, forcing studies to rely on limited building sets or single-variable perturbations that fail to capture real-world complexity. We present HOT (HVAC Operations Transfer), the first large-scale open-source dataset purpose-built for research into transfer learning in building control. HOT contains 159,744 unique building-weather combinations with systematic variations across envelope properties, occupancy patterns, and climate conditions spanning all 19 ASHRAE climate zones across 76 global locations. We formalise a comprehensive similarity-based framework with quantitative metrics for assessing transfer feasibility between source and target buildings across multiple context dimensions. Our key contributions: (1) a large-scale, open dataset and tooling enabling systematic, multi-variable transfer studies across 19 climate zones; (2) a quantitative similarity framework spanning geometry, thermal, climate, and function; and (3) zero-shot climate transfer experiments showing why realistic context variation matters for HVAC control.
Identifying indicators of consciousness in AI systems
Patrick Butlin
Robert Long
Tim Bayne
Jonathan Birch
David Chalmers
Axel Constant
George Deane
Stephen M. Fleming
Ryota Kanai
Colin Klein
Grace Lindsay
Matthias Michel
Liad Mudrik
Megan A.K. Peters
Eric Schwitzgebel
Jonathan Simon
Rufin Vanrullen
Scaling Latent Reasoning via Looped Language Models
Ruiming Zhu
Zixuan Wang
Kai Hua
Ziniu Li
Haoran Que
Boyi Wei
Zixin Wen
Fan Yin
He Xing
Li Li
Jiajun Shi
Kaijing Ma
Shanda Li
Taylor Kergan
Andrew C. Smith
Xin Qu
Mude Hui
Bohong Wu
Qiyang Min … (voir 13 de plus)
Hongzhi Huang
Xun Zhou
Wei Ye
Jiaheng Liu
Jian Yang 0030
Yunfeng Shi
Chenghua Lin
Enduo Zhao
Tianle Cai
Ge Zhang
Jason K. Eshraghian
Modern LLMs are trained to"think"primarily via explicit text generation, such as chain-of-thought (CoT), which defers reasoning to post-trai… (voir plus)ning and under-leverages pre-training data. We present and open-source Ouro, named after the recursive Ouroboros, a family of pre-trained Looped Language Models (LoopLM) that instead build reasoning into the pre-training phase through (i) iterative computation in latent space, (ii) an entropy-regularized objective for learned depth allocation, and (iii) scaling to 7.7T tokens. Ouro 1.4B and 2.6B models enjoy superior performance that match the results of up to 12B SOTA LLMs across a wide range of benchmarks. Through controlled experiments, we show this advantage stems not from increased knowledge capacity, but from superior knowledge manipulation capabilities. We also show that LoopLM yields reasoning traces more aligned with final outputs than explicit CoT. We hope our results show the potential of LoopLM as a novel scaling direction in the reasoning era. Our model is available here: http://ouro-llm.github.io.
Deep-learning-based virtual screening of antibacterial compounds
Gabriele Scalia
Steven T. Rutherford
Ziqing Lu
Kerry R. Buchholz
Nicholas Skelton
Kangway Chuang
Nathaniel Diamant
Jan-Christian Hütter
Jerome-Maxim Luescher
Anh Miu
Jeff Blaney
Leo Gendelev
Elizabeth Skippington
Greg Zynda
Nia Dickson
Aviv Regev
Man-Wah Tan
Tommaso Biancalani
Surrogate-based quantification of policy uncertainty in generative flow networks
Ram'on Nartallo-Kaluarachchi
Robert Manson-Sawko
Shashanka Ubaru
Dongsung Huh
Malgorzata J. Zimo'n
Lior Horesh
Learning What Matters: Steering Diffusion via Spectrally Anisotropic Forward Noise
Berton Earnshaw
Jason Hartford
Monte Carlo Tree Diffusion for System 2 Planning
Jaesik Yoon
Hyeonseo Cho
Doojin Baek
Diffusion models have recently emerged as a powerful tool for planning. However, unlike Monte Carlo Tree Search (MCTS)-whose performance nat… (voir plus)urally improves with inference-time computation scaling-standard diffusion-based planners offer only limited avenues for the scalability. In this paper, we introduce Monte Carlo Tree Diffusion (MCTD), a novel framework that integrates the generative strength of diffusion models with the adaptive search capabilities of MCTS. Our method reconceptualizes denoising as a tree-structured process, allowing partially denoised plans to be iteratively evaluated, pruned, and refined. By selectively expanding promising trajectories while retaining the flexibility to revisit and improve suboptimal branches, MCTD achieves the benefits of MCTS such as controlling exploration-exploitation trade-offs within the diffusion framework. Empirical results on challenging long-horizon tasks show that MCTD outperforms diffusion baselines, yielding higher-quality solutions as inference-time computation increases.
Towards a Formal Theory of Representational Compositionality
HVAC-SPICE: Value-Uncertainty In-Context RL with Thompson Sampling for Zero-Shot HVAC Control
Urban buildings consume 40\% of global energy, yet most rely on inefficient rule-based HVAC systems due to the impracticality of deploying a… (voir plus)dvanced controllers across diverse building stock. In-context reinforcement learning (ICRL) offers promise for rapid deployment without per-building training, but standard supervised learning objectives that maximise likelihood of training actions inherit behaviour-policy bias and provide weak exploration under the distribution shifts common when transferring across buildings and climates. We present SPICE (Sampling Policies In-Context with Ensemble uncertainty), a novel ICRL method specifically designed for zero-shot building control that addresses these fundamental limitations. SPICE introduces two key methodological innovations: (i) a propensity-corrected, return-aware training objective that prioritises high-advantage, high-uncertainty actions to enable improvement beyond suboptimal training demonstrations, and (ii) lightweight value ensembles with randomised priors that provide explicit uncertainty estimates for principled episode-level Thompson sampling. At deployment, SPICE samples one value head per episode and acts greedily, resulting in temporally coherent exploration without test-time gradients or building-specific models. We establish a comprehensive experimental protocol using the HOT dataset to evaluate SPICE across diverse building types and climate zones, focusing on the energy efficiency, occupant comfort, and zero-shot transfer capabilities that are critical for urban-scale deployment.