Yoshua Bengio

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

Mohammed Abukalam

Collaborateur·rice alumni - UdeM

agassoussisalwane2@gmail.com

Salwane Agassoussi

UdeM

Berkes Anaïs

Collaborateur·rice de recherche - Cambridge University

Superviseur⋅e principal⋅e :

Rim Assouel

Doctorat - UdeM

Ayoub Atanane

Collaborateur·rice alumni - Université du Québec à Rimouski

Stefan Bauer

Visiteur de recherche indépendant

Co-superviseur⋅e :

Guillaume Lajoie

Paul Bertin

Doctorat - UdeM

Ghait Boukachab

Collaborateur·rice alumni - UQAR

Shahana Chatterjee

Collaborateur·rice de recherche - N/A

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Collaborateur·rice de recherche - KAIST

Doctorat - UdeM

Doctorat - UdeM

Collaborateur·rice alumni - UdeM

Eric Elmoznino

Doctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Doctorat - UdeM

Doctorat - UdeM

Co-superviseur⋅e :

Leo Feng

Doctorat - UdeM

Stagiaire de recherche - UdeM

Ivan Grega

Stagiaire de recherche - UdeM

Pietro Greiner

Doctorat

Mohsin Hasan

Doctorat - UdeM

mohsin.hasan@mila.quebec

Edward Hu

Doctorat - UdeM

Moksh Jain

Doctorat - UdeM

moksh.jain@mila.quebec

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Collaborateur·rice alumni - UdeM

Hyeonah Kim

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Alex Hernandez

Minsu Kim

Stagiaire de recherche - UdeM

Collaborateur·rice de recherche - UdeM

Michał Koziarski

Collaborateur·rice alumni - UdeM

Salem Lahlou

Collaborateur·rice alumni - UdeM

Tabitha Edith Lee

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Seanie Lee

Collaborateur·rice alumni - UdeM

Collaborateur·rice alumni

Zhen Liu

Collaborateur·rice alumni - UdeM

Superviseur⋅e principal⋅e :

Liam Paull

Kanika Madan

Doctorat - UdeM

Mohammed Mahfoud

Collaborateur·rice alumni - UdeM

Nikolay Malkin

Collaborateur·rice alumni - UdeM

Cristian Dragos Manta

Doctorat - UdeM

Co-superviseur⋅e :

Dhanya Sridhar

Sören Mindermann

Collaborateur·rice de recherche - UdeM

Sarthak Mittal

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Visiteur de recherche indépendant - UdeM

Padideh Nouri

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Ali Parviz

Collaborateur·rice de recherche - Ying Wu Coll of Computing

Lena Podina

Doctorat - University of Waterloo

Superviseur⋅e principal⋅e :

David Rolnick

Camille Rochefort-Boulanger

Nassim Rahaman

Collaborateur·rice alumni - Max-Planck-Institute for Intelligent Systems

Doctorat - UdeM

Postdoctorat - UdeM

Visiteur de recherche indépendant - UdeM

Postdoctorat - UdeM

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Julie Hussin

Victor Schmidt

Collaborateur·rice alumni - UdeM

Postdoctorat - UdeM

Maîtrise recherche - UdeM

Marcin Sendera

Collaborateur·rice alumni - UdeM

Vedant Shah

Maîtrise recherche - UdeM

Postdoctorat

Marco Stock

Visiteur de recherche indépendant - Technical University of Munich

marco.stock@tum.de

Mélisande Astrid Crystal Teng

Doctorat - UdeM

Co-superviseur⋅e :

Collaborateur·rice de recherche - RWTH Aachen University (Rheinisch-Westfälische Technische Hochschule Aachen)

Superviseur⋅e principal⋅e :

David Rolnick

alexander.tong@mila.quebec

Alex Tong

Postdoctorat - UdeM

Postdoctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Collaborateur·rice de recherche - UdeM

Zichao Yan

Collaborateur·rice alumni - UdeM

Omar G. Younis

Collaborateur·rice de recherche

Collaborateur·rice de recherche - KAIST

Skipper : combiner l’abstraction spatiale et temporelle afin d’améliorer la généralisation

Nicole Zhang

Doctorat - McGill

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Harry Zhao

Doctorat - McGill

Superviseur⋅e principal⋅e :

Billets de blogue

Generic thumbnail for Mila Blog articles.

22 février 2024

par

Mingde Harry Zhao

Safa Alver

Harm van Seijen

Romain Laroche

Doina Precup

Yoshua Bengio

Mise à l’échelle au service du raisonnement et de l’apprentissage automatique basé sur un modèle

Scaling in the service of reasoning & model-based ML

4 avril 2023

par

Yoshua Bengio

Edward J. Hu

Une collaboration entre Mila et Relation Therapeutics pour découvrir in vitro de nouvelles associations médicamenteuses synergiques

A collaboration between Mila and Relation Therapeutics to discover novel synergistic combinations of drugs in vitro

23 mars 2022

par

Paul Bertin

Jake P. Taylor-King

Yoshua Bengio

Les réseaux de flot génératifs

15 mars 2022

par

Yoshua Bengio

Publications

Open Problems in Technical AI Governance

Anka Reuel

Benjamin Bucknall

Stephen Casper

Tim Fist

Lisa Soder

Onni Aarne

Lewis Hammond

Lujain Ibrahim

Alan Chan

Peter Wills

Markus Anderljung

Ben Garfinkel

Lennart Heim

Andrew Trask

Gabriel Mukobi

Rylan Schaeffer

Mauricio Baker

Sara Hooker

Irene Solaiman

Alexandra Luccioni … (voir 11 de plus)

Nitarshan Rajkumar

Nicolas Moes

Jeffrey Ladish

Neel Guha

Jessica Newman

Tobin South

Alex Pentland

Sanmi Koyejo

Mykel Kochenderfer

Robert F. Trager

AI progress is creating a growing range of risks and opportunities, but it is often unclear how they should be navigated. In many cases, the… (voir plus) barriers and uncertainties faced are at least partly technical. Technical AI governance, referring to technical analysis and tools for supporting the effective governance of AI, seeks to address such challenges. It can help to (a) identify areas where intervention is needed, (b) identify and assess the efficacy of potential governance actions, and (c) enhance governance options by designing mechanisms for enforcement, incentivization, or compliance. In this paper, we explain what technical AI governance is, why it is important, and present a taxonomy and incomplete catalog of its open problems. This paper is intended as a resource for technical researchers or research funders looking to contribute to AI governance.

2024-07-20

ArXiv (prépublication)

Redesigning Information Markets in the Era of Language Models

Martin Weiss

Nasim Rahaman

Manuel Wüthrich

Li Erran Li

Bernhard Schölkopf

Chris Pal

2024-07-10

colmweb.org/COLM/2024/Conference (accepté)

Improving Gradient-Guided Nested Sampling for Posterior Inference

Pablo Lemos

Nikolay Malkin

Will Handley

Laurence Perreault-Levasseur

Yashar Hezaveh

We present a performant, general-purpose gradient-guided nested sampling (GGNS) algorithm, combining the state of the art in differentiable … (voir plus)programming, Hamiltonian slice sampling, clustering, mode separation, dynamic nested sampling, and parallelization. This unique combination allows GGNS to scale well with dimensionality and perform competitively on a variety of synthetic and real-world problems. We also show the potential of combining nested sampling with generative flow networks to obtain large amounts of high-quality samples from the posterior distribution. This combination leads to faster mode discovery and more accurate estimates of the partition function.

2024-07-08

Proceedings of the 41st International Conference on Machine Learning (publié)

Memory Efficient Neural Processes via Constant Memory Attention Block

Leo Feng

Frederick Tung

Hossein Hajimirsadeghi

Mohamed Osama Ahmed

2024-07-08

Proceedings of the 41st International Conference on Machine Learning (publié)

proceedings.mlr.press

On Generalization for Generative Flow Networks

Anas Krichel

Nikolay Malkin

Salem Lahlou

Generative Flow Networks (GFlowNets) have emerged as an innovative learning paradigm designed to address the challenge of sampling from an u… (voir plus)nnormalized probability distribution, called the reward function. This framework learns a policy on a constructed graph, which enables sampling from an approximation of the target probability distribution through successive steps of sampling from the learned policy. To achieve this, GFlowNets can be trained with various objectives, each of which can lead to the model s ultimate goal. The aspirational strength of GFlowNets lies in their potential to discern intricate patterns within the reward function and their capacity to generalize effectively to novel, unseen parts of the reward function. This paper attempts to formalize generalization in the context of GFlowNets, to link generalization with stability, and also to design experiments that assess the capacity of these models to uncover unseen parts of the reward function. The experiments will focus on length generalization meaning generalization to states that can be constructed only by longer trajectories than those seen in training.

2024-07-03

ArXiv (prépublication)

Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold

Lazar Atanackovic

Xi Zhang

Brandon Amos

Mathieu Blanchette

Leo J Lee

Alexander Tong

Kirill Neklyudov

Numerous biological and physical processes can be modeled as systems of interacting samples evolving continuously over time, e.g. the dynami… (voir plus)cs of communicating cells or physical particles. Flow-based models allow for learning these dynamics at the population level --- they model the evolution of the entire distribution of samples. However, current flow-based models are limited to a single initial population and a set of predefined conditions which describe different dynamics. We propose

2024-06-17

ICML.cc/2024/Workshop/GRaM (publié)

Cell Morphology-Guided Small Molecule Generation with GFlowNets

Stephen Zhewen Lu

Ziqing Lu

Ehsan Hajiramezanali

Tommaso Biancalani

Gabriele Scalia

Michał Koziarski

2024-06-16

ICML.cc/2024/Workshop/AI4Science (poster)

MAP: Low-compute Model Merging with Amortized Pareto Fronts via Quadratic Approximation

Lu Li

Tianyu Zhang

Zhiqi Bu

Suyuchen Wang

Huan He

Jie Fu

Yonghui Wu

Jiang Bian

Yong Chen

Model merging has emerged as an effective approach to combine multiple single-task models into a multitask model. This process typically inv… (voir plus)olves computing a weighted average of the model parameters without any additional training. Existing model-merging methods focus on enhancing average task accuracy. However, interference and conflicts between the objectives of different tasks can lead to trade-offs during the merging process. In real-world applications, a set of solutions with various trade-offs can be more informative, helping practitioners make decisions based on diverse preferences. In this paper, we introduce a novel and low-compute algorithm, Model Merging with Amortized Pareto Front (MAP). MAP efficiently identifies a Pareto set of scaling coefficients for merging multiple models, reflecting the trade-offs involved. It amortizes the substantial computational cost of evaluations needed to estimate the Pareto front by using quadratic approximation surrogate models derived from a pre-selected set of scaling coefficients. Experimental results on vision and natural language processing tasks demonstrate that MAP can accurately identify the Pareto front, providing practitioners with flexible solutions to balance competing task objectives. We also introduce Bayesian MAP for scenarios with a relatively low number of tasks and Nested MAP for situations with a high number of tasks, further reducing the computational cost of evaluation.

2024-06-11

ArXiv (prépublication)

VCR: Visual Caption Restoration

Tianyu Zhang

Suyuchen Wang

Lu Li

Ge Zhang

Perouz Taslakian

Sai Rajeswar

Jie Fu

Bang Liu

We introduce Visual Caption Restoration (VCR), a novel vision-language task that challenges models to accurately restore partially obscured … (voir plus)texts using pixel-level hints within images. This task stems from the observation that text embedded in images is intrinsically different from common visual elements and natural language due to the need to align the modalities of vision, text, and text embedded in images. While numerous works have integrated text embedded in images into visual question-answering tasks, approaches to these tasks generally rely on optical character recognition or masked language modeling, thus reducing the task to mainly text-based processing. However, text-based processing becomes ineffective in VCR as accurate text restoration depends on the combined information from provided images, context, and subtle cues from the tiny exposed areas of masked texts. We develop a pipeline to generate synthetic images for the VCR task using image-caption pairs, with adjustable caption visibility to control the task difficulty. With this pipeline, we construct a dataset for VCR called VCR-Wiki using images with captions from Wikipedia, comprising 2.11M English and 346K Chinese entities in both easy and hard split variants. Our results reveal that current vision language models significantly lag behind human performance in the VCR task, and merely fine-tuning the models on our dataset does not lead to notable improvements. We release VCR-Wiki and the data construction code to facilitate future research.

2024-06-10

ArXiv (prépublication)

VCR: Visual Caption Restoration

Tianyu Zhang

Suyuchen Wang

Lu Li

Ge Zhang

Perouz Taslakian

Sai Rajeswar

Jie Fu

Bang Liu

2024-06-10

ArXiv (prépublication)

VCR: Visual Caption Restoration

Tianyu Zhang

Suyuchen Wang

Lu Li

Ge Zhang

Perouz Taslakian

Sai Rajeswar

Jie Fu

Bang Liu

2024-06-10

ArXiv (prépublication)

Amortizing intractable inference in diffusion models for vision, language, and control

Siddarth Venkatraman

Moksh J. Jain

Luca Scimeca

Minsu Kim

Marcin Sendera

Mohsin Hasan

Luke Rowe

Sarthak Mittal

Pablo Lemos

Emmanuel Bengio

Alexandre Adam

Jarrid Rector-Brooks

Glen Berseth

Nikolay Malkin

Diffusion models have emerged as effective distribution estimators in vision, language, and reinforcement learning, but their use as priors … (voir plus)in downstream tasks poses an intractable posterior inference problem. This paper studies amortized sampling of the posterior over data,

2024-05-31

ArXiv (prépublication)