Yoshua Bengio

jamal.abouhaibeh@mila.quebec

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

Collaborateur·rice de recherche - Cambridge University

Superviseur⋅e principal⋅e :

David Rolnick

berkes.anais@mila.quebec

sanghyeok.choi@mila.quebec

Rim Assouel

Doctorat - UdeM

assouelr@mila.quebec

Xiaoyin Chen

Doctorat - UdeM

xiaoyin.chen@mila.quebec

Sanghyeok Choi

Collaborateur·rice de recherche - KAIST

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

Mohsin Hasan

Doctorat - UdeM

mohsin.hasan@mila.quebec

thomas.jiralerspong@mila.quebec

Moksh Jain

Doctorat - UdeM

moksh.jain@mila.quebec

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Guillaume Lajoie

Collaborateur·rice alumni - UdeM

younesse.kaddar@mila.quebec

Minsu Kim

Collaborateur·rice de recherche - UdeM

minsu.kim@mila.quebec

Hyeonah Kim

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Alex Hernández-García

hyeonah.kim@mila.quebec

Jeongjae Lee

Collaborateur·rice de recherche - KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY

jeongjae.lee@mila.quebec

Collaborateur·rice de recherche

Song LIU

Collaborateur·rice de recherche - s.o.

liusong299@gmail.com

cristian-dragos.manta@mila.quebec

Cristian Dragos Manta

Doctorat - UdeM

Co-superviseur⋅e :

Dhanya Sridhar

Sarthak Mittal

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Visiteur de recherche indépendant - UdeM

nguyenph@mila.quebec

Ali Parviz

Collaborateur·rice de recherche - Ying Wu Coll of Computing

ali.parviz@mila.quebec

Lena Podina

Collaborateur·rice de recherche - University of Waterloo

Superviseur⋅e principal⋅e :

David Rolnick

elena.podina@mila.quebec

jarrid.rector-brooks@mila.quebec

Jarrid Rector-Brooks

Doctorat - UdeM

Danyal REHMAN

Postdoctorat - UdeM

danyal.rehman@mila.quebec

oliver.richardson@mila.quebec

Oli RICHARDSON

Postdoctorat - UdeM

Camille Rochefort-Boulanger

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Postdoctorat - UdeM

Co-superviseur⋅e :

Alex Hernández-García

divya.sharma@mila.quebec

Mélisande Astrid Crystal Teng

Collaborateur·rice alumni - UdeM

Co-superviseur⋅e :

Alex Tong

Collaborateur·rice alumni - UdeM

alexander.tong@mila.quebec

Collaborateur·rice de recherche - University of Cambridge

oszkar.urban@mila.quebec

donna.vakalis@mila.quebec

Donna Vakalis

Collaborateur·rice alumni - UdeM

Co-superviseur⋅e :

David Rolnick

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Glen Berseth

siddarth.venkatraman@mila.quebec

Collaborateur·rice de recherche

joseph.viviano2@mila.quebec

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

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

Tianyu Zhang

Doctorat - UdeM

tianyu.zhang@mila.quebec

Doctorat - McGill

Superviseur⋅e principal⋅e :

Harry Zhao

Collaborateur·rice alumni - 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

International Institutions for Advanced AI

Lewis Ho

Joslyn N. Barnhart

Robert Trager

Miles Brundage

Allison Sovey Carnegie

Rumman Chowdhury

Allan Dafoe

Gillian K. Hadfield

Margaret Levi

D. Snidal

2023-07-09

ArXiv (prépublication)

Generative Adversarial Nets

Moez Krichen

Ian J. Goodfellow

Mehdi Mirza

Generative Adversarial Networks (GANs) are very popular frameworks for generating high-quality data, and are immensely used in both the acad… (voir plus)emia and industry in many domains. Arguably, their most substantial impact has been in the area of computer vision, where they achieve state-of-the-art image generation. This chapter gives an introduction to GANs, by discussing their principle mechanism and presenting some of their inherent problems during training and evaluation. We focus on these three issues: (1) mode collapse, (2) vanishing gradients, and (3) generation of low-quality images. We then list some architecture-variant and loss-variant GANs that remedy the above challenges. Lastly, we present two utilization examples of GANs for real-world applications: Data augmentation and face images generation.

2023-07-05

2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT) (publié)

Generative Flow Networks: a Markov Chain Perspective

Tristan Deleu

2023-07-03

ArXiv (prépublication)

Better Training of GFlowNets with Local Credit and Incomplete Trajectories

Ling Pan

Nikolay Malkin

Dinghuai Zhang

Generative Flow Networks or GFlowNets are related to Monte-Carlo Markov chain methods (as they sample from a distribution specified by an en… (voir plus)ergy function), reinforcement learning (as they learn a policy to sample composed objects through a sequence of steps), generative models (as they learn to represent and sample from a distribution) and amortized variational methods (as they can be used to learn to approximate and sample from an otherwise intractable posterior, given a prior and a likelihood). They are trained to generate an object

2023-07-02

Proceedings of the 40th International Conference on Machine Learning (publié)

Hyena Hierarchy: Towards Larger Convolutional Language Models

Michael Poli

Stefano Massaroli

Eric Nguyen

Daniel Y Fu

Tri Dao

Stephen Baccus

Stefano Ermon

Christopher Re

2023-07-02

Proceedings of the 40th International Conference on Machine Learning (publié)

MixupE: Understanding and improving Mixup from directional derivative perspective

Vikas Verma

Yingtian Zou

Sarthak Mittal

Wai Hoh Tang

Hieu Pham

Juho Kannala

Arno Solin

Kenji Kawaguchi

2023-07-01

Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence (publié)

Stochastic Generative Flow Networks

Longbo Huang

2023-07-01

Conference on Uncertainty in Artificial Intelligence (publié)

Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization for Heterogeneous Representational Coarseness

Dianbo Liu

Alex Lamb

Xu Ji

Pascal Junior Tikeng Notsawo

Michael Mozer

Kenji Kawaguchi

Vector Quantization (VQ) is a method for discretizing latent representations and has become a major part of the deep learning toolkit. It ha… (voir plus)s been theoretically and empirically shown that discretization of representations leads to improved generalization, including in reinforcement learning where discretization can be used to bottleneck multi-agent communication to promote agent specialization and robustness. The discretization tightness of most VQ-based methods is defined by the number of discrete codes in the representation vector and the codebook size, which are fixed as hyperparameters. In this work, we propose learning to dynamically select discretization tightness conditioned on inputs, based on the hypothesis that data naturally contains variations in complexity that call for different levels of representational coarseness which is observed in many heterogeneous data sets. We show that dynamically varying tightness in communication bottlenecks can improve model performance on visual reasoning and reinforcement learning tasks with heterogeneity in representations.

2023-06-25

AAAI Conference on Artificial Intelligence (publié)

The Effect of Diversity in Meta-Learning

Ramnath Kumar

Tristan Deleu

Recent studies show that task distribution plays a vital role in the meta-learner's performance. Conventional wisdom is that task diversity … (voir plus)should improve the performance of meta-learning. In this work, we find evidence to the contrary; (i) our experiments draw into question the efficacy of our learned models: similar manifolds can be learned with a subset of the data (lower task diversity). This finding questions the advantage of providing more data to the model, and (ii) adding diversity to the task distribution (higher task diversity) sometimes hinders the model and does not lead to a significant improvement in performance as previously believed. To strengthen our findings, we provide both empirical and theoretical evidence.

2023-06-25

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

Constant Memory Attention Block

Leo Feng

Frederick Tung

Hossein Hajimirsadeghi

Mohamed Osama Ahmed

2023-06-19

ICML.cc/2023/Workshop/ES-FoMO (poster)

GFlowNets for Causal Discovery: an Overview

Dragos Cristian Manta

Edward J Hu

2023-06-19

ICML.cc/2023/Workshop/SODS (poster)

BatchGFN: Generative Flow Networks for Batch Active Learning

Shreshth A Malik

Salem Lahlou

Andrew Jesson

Moksh J. Jain

Nikolay Malkin

Tristan Deleu

Yarin Gal

We introduce BatchGFN—a novel approach for pool-based active learning that uses generative flow networks to sample sets of data points pro… (voir plus)portional to a batch reward. With an appropriate reward function to quantify the utility of acquiring a batch, such as the joint mutual information between the batch and the model parameters, BatchGFN is able to construct highly informative batches for active learning in a principled way. We show our approach enables sampling near-optimal utility batches at inference time with a single forward pass per point in the batch in toy regression problems. This alleviates the computational complexity of batch-aware algorithms and removes the need for greedy approximations to find maximizers for the batch reward. We also present early results for amortizing training across acquisition steps, which will enable scaling to real-world tasks.

2023-06-18

ICML.cc/2023/Workshop/SPIGM (poster)