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

Jamal Abou Haibeh

Collaborateur·rice alumni - McGill

jamal.abouhaibeh@mila.quebec

Collaborateur·rice de recherche - Cambridge University

Superviseur⋅e principal⋅e :

berkes.anais@mila.quebec

Doctorat - UdeM

assouelr@mila.quebec

Shahana Chatterjee

Collaborateur·rice de recherche - N/A

Superviseur⋅e principal⋅e :

shahana.chatterjee@mila.quebec

Doctorat - UdeM

xiaoyin.chen@mila.quebec

Collaborateur·rice de recherche - KAIST

sanghyeok.choi@mila.quebec

Aniket Didolkar

Doctorat - UdeM

aniket.didolkar@mila.quebec

Doctorat - UdeM

Co-superviseur⋅e :

Guillaume Lajoie

eric.elmoznino@mila.quebec

Doctorat - UdeM

lena-nehale.ezzine@mila.quebec

Jean-Pierre Falet

Doctorat - UdeM

jean-pierre.falet@mila.quebec

Doctorat

pietro.greiner@mila.quebec

Doctorat - UdeM

mohsin.hasan@mila.quebec

Doctorat - UdeM

moksh.jain@mila.quebec

Thomas Jiralerspong

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Guillaume Lajoie

thomas.jiralerspong@mila.quebec

Younesse Kaddar

Collaborateur·rice alumni - UdeM

younesse.kaddar@mila.quebec

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Alex Hernández-García

hyeonah.kim@mila.quebec

Collaborateur·rice de recherche - UdeM

minsu.kim@mila.quebec

Stagiaire de recherche - KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY

jeongjae.lee@mila.quebec

Collaborateur·rice de recherche - s.o.

liusong299@gmail.com

Collaborateur·rice de recherche

liucheng@mila.quebec

Cristian Dragos Manta

Doctorat - UdeM

Co-superviseur⋅e :

cristian-dragos.manta@mila.quebec

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Guillaume Lajoie

mittalsa@mila.quebec

Visiteur de recherche indépendant - UdeM

nguyenph@mila.quebec

Collaborateur·rice de recherche - Ying Wu Coll of Computing

ali.parviz@mila.quebec

Collaborateur·rice de recherche - University of Waterloo

Superviseur⋅e principal⋅e :

elena.podina@mila.quebec

Jarrid Rector-Brooks

Doctorat - UdeM

jarrid.rector-brooks@mila.quebec

Postdoctorat - UdeM

danyal.rehman@mila.quebec

Postdoctorat - UdeM

oliver.richardson@mila.quebec

Camille Rochefort-Boulanger

Doctorat - UdeM

Superviseur⋅e principal⋅e :

rochefoc@mila.quebec

Postdoctorat - UdeM

Co-superviseur⋅e :

Alex Hernández-García

divya.sharma@mila.quebec

Mélisande Astrid Crystal Teng

Collaborateur·rice alumni - UdeM

Co-superviseur⋅e :

Hugo Larochelle

tengmeli@mila.quebec

Collaborateur·rice alumni - UdeM

alexander.tong@mila.quebec

Collaborateur·rice de recherche - University of Cambridge

oszkar.urban@mila.quebec

Collaborateur·rice alumni - UdeM

Co-superviseur⋅e :

donna.vakalis@mila.quebec

Siddarth Venkatraman

Doctorat - UdeM

Superviseur⋅e principal⋅e :

siddarth.venkatraman@mila.quebec

Esmeralda Whitammer

Collaborateur·rice de recherche - UdeM

esmeralda.whitammer@mila.quebec

Collaborateur·rice alumni

omar.younis@mila.quebec

Collaborateur·rice de recherche - UdeM

taeyoung.yun@mila.quebec

Doctorat - UdeM

tianyu.zhang@mila.quebec

Doctorat - McGill

Superviseur⋅e principal⋅e :

Mathieu Blanchette

xi.zhang@mila.quebec

Billets de blogue

Generic thumbnail for Mila Blog articles.

22 février 2024

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

par

Mingde Harry Zhao

Safa Alver

Harm van Seijen

Romain Laroche

Scaling in the service of reasoning & model-based ML

4 avril 2023

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

par

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

23 mars 2022

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

par

Jake P. Taylor-King

Generative Flow Networks

15 mars 2022

Les réseaux de flot génératifs

par

Publications

Causal Inference in Gene Regulatory Networks with GFlowNet: Towards Scalability in Large Systems

Trang Nguyen

Dianbo Liu

Understanding causal relationships within Gene Regulatory Networks (GRNs) is essential for unraveling the gene interactions in cellular proc… (voir plus)esses. However, causal discovery in GRNs is a challenging problem for multiple reasons including the existence of cyclic feedback loops and uncertainty that yields diverse possible causal structures. Previous works in this area either ignore cyclic dynamics (assume acyclic structure) or struggle with scalability. We introduce Swift-DynGFN as a novel framework that enhances causal structure learning in GRNs while addressing scalability concerns. Specifically, Swift-DynGFN exploits gene-wise independence to boost parallelization and to lower computational cost. Experiments on real single-cell RNA velocity and synthetic GRN datasets showcase the advancement in learning causal structure in GRNs and scalability in larger systems.

2023-10-04

ArXiv (prépublication)

Leveraging Diffusion Disentangled Representations to Mitigate Shortcuts in Underspecified Visual Tasks

Alexander Rubinstein

Armand Mihai Nicolicioiu

Damien Teney

Spurious correlations in the data, where multiple cues are predictive of the target labels, often lead to shortcut learning phenomena, where… (voir plus) a model may rely on erroneous, easy-to-learn, cues while ignoring reliable ones. In this work, we propose an ensemble diversification framework exploiting the generation of synthetic counterfactuals using Diffusion Probabilistic Models (DPMs). We discover that DPMs have the inherent capability to represent multiple visual cues independently, even when they are largely correlated in the training data. We leverage this characteristic to encourage model diversity and empirically show the efficacy of the approach with respect to several diversification objectives. We show that diffusion-guided diversification can lead models to avert attention from shortcut cues, achieving ensemble diversity performance comparable to previous methods requiring additional data collection.

2023-10-02

ArXiv (prépublication)

AI and Catastrophic Risk

2023-09-30

Journal of Democracy (publié)

RECOVER identifies synergistic drug combinations in vitro through sequential model optimization

Jarrid Rector-Brooks

Thomas Gaudelet

Andrew Anighoro

Torsten Gross

Francisco Martínez-Peña

Eileen L. Tang

M.S. Suraj

Cristian Regep

Jeremy B.R. Hayter

Maksym Korablyov

Nicholas Valiante

Almer Van Der Sloot

Mike Tyers

Charles E.S. Roberts

Michael M. Bronstein

Luke L. Lairson

Jake P. Taylor-King

2023-09-30

Cell Reports Methods (publié)

HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution

Eric Nguyen

Michael Poli

Marjan Faizi

Armin W Thomas

Callum Birch-Sykes

Michael Wornow

Aman Patel

Clayton M. Rabideau

Stefano Massaroli

Stefano Ermon

Stephen Baccus

Christopher Re

2023-09-20

NeurIPS.cc/2023/Conference (spotlight)

Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions

Stefano Massaroli

Michael Poli

Daniel Y Fu

Hermann Kumbong

Rom Nishijima Parnichkun

Aman Timalsina

David W. Romero

Quinn McIntyre

Beidi Chen

Atri Rudra

Ce Zhang

Christopher Re

Stefano Ermon

Recent advances in attention-free sequence models rely on convolutions as alternatives to the attention operator at the core of Transformers… (voir plus). In particular, long convolution sequence models have achieved state-of-the-art performance in many domains, but incur a significant cost during auto-regressive inference workloads -- naively requiring a full pass (or caching of activations) over the input sequence for each generated token -- similarly to attention-based models. In this paper, we seek to enable

2023-09-20

NeurIPS.cc/2023/Conference (poster)

Neural Causal Structure Discovery from Interventions

Nan Rosemary Ke

Hugo Larochelle

Bernhard Schölkopf

Michael Curtis Mozer

Christopher Pal

Recent promising results have generated a surge of interest in continuous optimization methods for causal discovery from observational data.… (voir plus) However, there are theoretical limitations on the identifiability of underlying structures obtained solely from observational data. Interventional data, on the other hand, provides richer information about the underlying data-generating process. Nevertheless, extending and applying methods designed for observational data to include interventions is a challenging problem. To address this issue, we propose a general framework based on neural networks to develop models that incorporate both observational and interventional data. Notably, our method can handle the challenging and realistic scenario where the identity of the intervened upon variable is unknown. We evaluate our proposed approach in the context of graph recovery, both de novo and from a partially-known edge set. Our method achieves strong benchmark results on various structure learning tasks, including structure recovery of synthetic graphs as well as standard graphs from the Bayesian Network Repository.

2023-09-09

TMLR (accepté)

Consciousness in Artificial Intelligence: Insights from the Science of Consciousness

Patrick Mark Butlin

Run Long

Jonathan C. P. Birch

Axel Constant

George Deane

S. Fleming

C. Frith

Xuanxiu Ji

Ryota Kanai

C. Klein

Grace W. Lindsay

Matthias Michel

Liad Mudrik

Megan A. K. Peters

Eric Schwitzgebel

Jonathan Simon

Rufin Vanrullen

2023-08-16

ArXiv (prépublication)

Scientific discovery in the age of artificial intelligence

Hanchen Wang

Tianfan Fu

Yuanqi Du

Wenhao Gao

Kexin Huang

Ziming Liu

Payal Chandak

Peter Van Katwyk

Andreea Deac

Animashree Anandkumar

K. Bergen

Carla P. Gomes

Shirley Ho

Pushmeet Kohli

Joan Lasenby

Jure Leskovec

Tie-Yan Liu

A. Manrai

Debora Susan Marks … (voir 10 de plus)

Bharath Ramsundar

Le Song

Jimeng Sun

Petar Veličković

MAX WELLING

Linfeng Zhang

Connor Wilson. Coley

Marinka Žitnik

2023-08-01

Nature (publié)

Discrete Key-Value Bottleneck

Frederik Träuble

Nasim Rahaman

Michael Mozer

Kenji Kawaguchi

Bernhard Schölkopf

Deep neural networks perform well on classification tasks where data streams are i.i.d. and labeled data is abundant. Challenges emerge with… (voir plus) non-stationary training data streams such as continual learning. One powerful approach that has addressed this challenge involves pre-training of large encoders on volumes of readily available data, followed by task-specific tuning. Given a new task, however, updating the weights of these encoders is challenging as a large number of weights needs to be fine-tuned, and as a result, they forget information about the previous tasks. In the present work, we propose a model architecture to address this issue, building upon a discrete bottleneck containing pairs of separate and learnable key-value codes. Our paradigm will be to encode; process the representation via a discrete bottleneck; and decode. Here, the input is fed to the pre-trained encoder, the output of the encoder is used to select the nearest keys, and the corresponding values are fed to the decoder to solve the current task. The model can only fetch and re-use a sparse number of these key-value pairs during inference, enabling localized and context-dependent model updates. We theoretically investigate the ability of the discrete key-value bottleneck to minimize the effect of learning under distribution shifts and show that it reduces the complexity of the hypothesis class. We empirically verify the proposed method under challenging class-incremental learning scenarios and show that the proposed model - without any task boundaries - reduces catastrophic forgetting across a wide variety of pre-trained models, outperforming relevant baselines on this task.

2023-07-24

International Conference on Machine Learning (Accept (Poster))

proceedings.mlr.press

Equivariance with Learned Canonicalization Functions

Sékou-Oumar Kaba

Arnab Kumar Mondal

Siamak Ravanbakhsh

Symmetry-based neural networks often constrain the architecture in order to achieve invariance or equivariance to a group of transformations… (voir plus). In this paper, we propose an alternative that avoids this architectural constraint by learning to produce canonical representations of the data. These canonicalization functions can readily be plugged into non-equivariant backbone architectures. We offer explicit ways to implement them for some groups of interest. We show that this approach enjoys universality while providing interpretable insights. Our main hypothesis, supported by our empirical results, is that learning a small neural network to perform canonicalization is better than using predefined heuristics. Our experiments show that learning the canonicalization function is competitive with existing techniques for learning equivariant functions across many tasks, including image classification,

2023-07-24

ICML (Accept (Poster))

proceedings.mlr.press

FAENet: Frame Averaging Equivariant GNN for Materials Modeling

Alexandre Duval

Alex Hernandez Garcia

Santiago Miret

Fragkiskos D. Malliaros

Applications of machine learning techniques for materials modeling typically involve functions known to be equivariant or invariant to speci… (voir plus)fic symmetries. While graph neural networks (GNNs) have proven successful in such tasks, they enforce symmetries via the model architecture, which often reduces their expressivity, scalability and comprehensibility. In this paper, we introduce (1) a flexible framework relying on stochastic frame-averaging (SFA) to make any model E(3)-equivariant or invariant through data transformations. (2) FAENet: a simple, fast and expressive GNN, optimized for SFA, that processes geometric information without any symmetrypreserving design constraints. We prove the validity of our method theoretically and empirically demonstrate its superior accuracy and computational scalability in materials modeling on the OC20 dataset (S2EF, IS2RE) as well as common molecular modeling tasks (QM9, QM7-X). A package implementation is available at https://faenet.readthedocs.io.

2023-07-24

International Conference on Machine Learning (Accept (Poster))

proceedings.mlr.press