Yoshua Bengio

Biographie

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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

Berkes Anaïs

Collaborateur·rice de recherche - Cambridge University

Superviseur⋅e principal⋅e :

Rim Assouel

Doctorat - UdeM

Junyeob BAEK

Visiteur de recherche indépendant - KAIST

Visiteur de recherche indépendant

Co-superviseur⋅e :

Guillaume Lajoie

Paul Bertin

Doctorat - UdeM

Shahana Chatterjee

Collaborateur·rice de recherche - N/A

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Collaborateur·rice de recherche - KAIST

Doctorat - UdeM

Doctorat - UdeM

Stagiaire de recherche - UdeM

Co-superviseur⋅e :

Loubna Benabbou

Eric Elmoznino

Doctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Doctorat - UdeM

Co-superviseur⋅e :

Leo Feng

Doctorat - UdeM

leo.feng@mila.quebec

Ivan Grega

Stagiaire de recherche - UdeM

Doctorat

Doctorat - UdeM

mohsin.hasan@mila.quebec

Edward Hu

Doctorat - UdeM

Moksh Jain

Doctorat - UdeM

moksh.jain@mila.quebec

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Collaborateur·rice alumni - UdeM

Hyeonah Kim

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Alex Hernandez

Yaroslav KIVVA

Collaborateur·rice de recherche - 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

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

Camille Rochefort-Boulanger

Lena Podina

Doctorat - University of Waterloo

Superviseur⋅e principal⋅e :

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

Amine RAZIG

Stagiaire de recherche - UdeM

Co-superviseur⋅e :

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 :

Hugo Larochelle

alexander.tong@mila.quebec

Alex Tong

Postdoctorat - UdeM

Postdoctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Collaborateur·rice de recherche - 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

Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning

Sébastien Lachapelle

Tristan Deleu

Divyat Mahajan

Ioannis Mitliagkas

Simon Lacoste-Julien

Quentin Bertrand

Although disentangled representations are often said to be beneficial for downstream tasks, current empirical and theoretical understanding … (voir plus)is limited. In this work, we provide evidence that disentangled representations coupled with sparse base-predictors improve generalization. In the context of multi-task learning, we prove a new identifiability result that provides conditions under which maximally sparse base-predictors yield disentangled representations. Motivated by this theoretical result, we propose a practical approach to learn disentangled representations based on a sparsity-promoting bi-level optimization problem. Finally, we explore a meta-learning version of this algorithm based on group Lasso multiclass SVM base-predictors, for which we derive a tractable dual formulation. It obtains competitive results on standard few-shot classification benchmarks, while each task is using only a fraction of the learned representations.

2022-11-26

ArXiv (prépublication)

Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning

Sébastien Lachapelle

Tristan Deleu

Divyat Mahajan

Ioannis Mitliagkas

Simon Lacoste-Julien

Quentin Bertrand

2022-11-26

ArXiv (prépublication)

PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design

Alexandre AGM Duval

Victor Schmidt

Santiago Miret

Alex Hernandez-Garcia

David Rolnick

Mitigating the climate crisis requires a rapid transition towards lower-carbon energy. Catalyst materials play a crucial role in the electro… (voir plus)chemical reactions involved in numerous industrial processes key to this transition, such as renewable energy storage and electrofuel synthesis. To reduce the energy spent on such activities, we must quickly discover more efficient catalysts to drive electrochemical reactions. Machine learning (ML) holds the potential to efficiently model materials properties from large amounts of data, accelerating electrocatalyst design. The Open Catalyst Project OC20 dataset was constructed to that end. However, ML models trained on OC20 are still neither scalable nor accurate enough for practical applications. In this paper, we propose task-specific innovations applicable to most architectures, enhancing both computational efficiency and accuracy. This includes improvements in (1) the graph creation step, (2) atom representations, (3) the energy prediction head, and (4) the force prediction head. We describe these contributions, referred to as PhAST, and evaluate them thoroughly on multiple architectures. Overall, PhAST improves energy MAE by 4 to 42

2022-11-22

ArXiv (prépublication)

Object-centric causal representation learning

Amin Mansouri

Jason Hartford

Kartik Ahuja

2022-11-07

NeurIPS.cc/2022/Workshop/NeurReps (poster)

Laurence Perreault-Levasseur

Posterior samples of source galaxies in strong gravitational lenses with score-based priors

Alexandre Adam

Adam Coogan

Nikolay Malkin

Ronan Legin

Yashar Hezaveh

2022-11-07

ArXiv (prépublication)

Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayes

Mizu Nishikawa-Toomey

Tristan Deleu

Jithendaraa Subramanian

Laurent Charlin

Bayesian causal structure learning aims to learn a posterior distribution over directed acyclic graphs (DAGs), and the mechanisms that defin… (voir plus)e the relationship between parent and child variables. By taking a Bayesian approach, it is possible to reason about the uncertainty of the causal model. The notion of modelling the uncertainty over models is particularly crucial for causal structure learning since the model could be unidentifiable when given only a finite amount of observational data. In this paper, we introduce a novel method to jointly learn the structure and mechanisms of the causal model using Variational Bayes, which we call Variational Bayes-DAG-GFlowNet (VBG). We extend the method of Bayesian causal structure learning using GFlowNets to learn not only the posterior distribution over the structure, but also the parameters of a linear-Gaussian model. Our results on simulated data suggest that VBG is competitive against several baselines in modelling the posterior over DAGs and mechanisms, while offering several advantages over existing methods, including the guarantee to sample acyclic graphs, and the flexibility to generalize to non-linear causal mechanisms.

2022-11-04

ArXiv (prépublication)

A General-Purpose Neural Architecture for Geospatial Systems

Nasim Rahaman

Martin Weiss

Frederik Träuble

Francesco Locatello

Alexandre Lacoste

Chris Pal

Li Erran Li

Bernhard Schölkopf

2022-11-02

OpenReview.net/Anonymous_Preprint (inconnu)

Consistent Training via Energy-Based GFlowNets for Modeling Discrete Joint Distributions

Chanakya Ajit Ekbote

Moksh J. Jain

Payel Das

Generative Flow Networks (GFlowNets) have demonstrated significant performance improvements for generating diverse discrete objects …

2022-11-01

ArXiv (prépublication)

Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning

Riashat Islam

Hongyu Zang

Anirudh Goyal

Alex Lamb

Kenji Kawaguchi

Xin Li

Romain Laroche

Remi Tachet des Combes

2022-11-01

ArXiv (prépublication)

Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints

Jose Gallego-Posada

Juan Ramirez

Akram Erraqabi

Simon Lacoste-Julien

The performance of trained neural networks is robust to harsh levels of pruning. Coupled with the ever-growing size of deep learning models,… (voir plus) this observation has motivated extensive research on learning sparse models. In this work, we focus on the task of controlling the level of sparsity when performing sparse learning. Existing methods based on sparsity-inducing penalties involve expensive trial-and-error tuning of the penalty factor, thus lacking direct control of the resulting model sparsity. In response, we adopt a constrained formulation: using the gate mechanism proposed by Louizos et al. (2018), we formulate a constrained optimization problem where sparsification is guided by the training objective and the desired sparsity target in an end-to-end fashion. Experiments on CIFAR-{10, 100}, TinyImageNet, and ImageNet using WideResNet and ResNet{18, 50} models validate the effectiveness of our proposal and demonstrate that we can reliably achieve pre-determined sparsity targets without compromising on predictive performance.

MAgNet: Mesh Agnostic Neural PDE Solver

Oussama Boussif

Dan Assouline

Loubna Benabbou

The computational complexity of classical numerical methods for solving Partial Differential Equations (PDE) scales significantly as the res… (voir plus)olution increases. As an important example, climate predictions require fine spatio-temporal resolutions to resolve all turbulent scales in the fluid simulations. This makes the task of accurately resolving these scales computationally out of reach even with modern supercomputers. As a result, current numerical modelers solve PDEs on grids that are too coarse (3km to 200km on each side), which hinders the accuracy and usefulness of the predictions. In this paper, we leverage the recent advances in Implicit Neural Representations (INR) to design a novel architecture that predicts the spatially continuous solution of a PDE given a spatial position query. By augmenting coordinate-based architectures with Graph Neural Networks (GNN), we enable zero-shot generalization to new non-uniform meshes and long-term predictions up to 250 frames ahead that are physically consistent. Our Mesh Agnostic Neural PDE Solver (MAgNet) is able to make accurate predictions across a variety of PDE simulation datasets and compares favorably with existing baselines. Moreover, MAgNet generalizes well to different meshes and resolutions up to four times those trained on.

Multi-Objective GFlowNets

Moksh J. Jain

Sharath Chandra Raparthy

Alex Hernandez-Garcia

Jarrid Rector-Brooks

Santiago Miret

Emmanuel Bengio

We study the problem of generating diverse candidates in the context of Multi-Objective Optimization. In many applications of machine learni… (voir plus)ng such as drug discovery and material design, the goal is to generate candidates which simultaneously optimize a set of potentially conflicting objectives. Moreover, these objectives are often imperfect evaluations of some underlying property of interest, making it important to generate diverse candidates to have multiple options for expensive downstream evaluations. We propose Multi-Objective GFlowNets (MOGFNs), a novel method for generating diverse Pareto optimal solutions, based on GFlowNets. We introduce two variants of MOGFNs: MOGFN-PC, which models a family of independent sub-problems defined by a scalarization function, with reward-conditional GFlowNets, and MOGFN-AL, which solves a sequence of sub-problems defined by an acquisition function in an active learning loop. Our experiments on wide variety of synthetic and benchmark tasks demonstrate advantages of the proposed methods in terms of the Pareto performance and importantly, improved candidate diversity, which is the main contribution of this work.

2022-10-23

ArXiv (prépublication)