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
Fondateur et Conseiller scientifique, Équipe de direction
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
Site web
Google Scholar

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.

Publications

Generalization in Machine Learning via Analytical Learning Theory
Kenji Kawaguchi
Yoshua Bengio
This paper introduces a novel measure-theoretic theory for machine learning that does not require statistical assumptions. Based on this the… (voir plus)ory, a new regularization method in deep learning is derived and shown to outperform previous methods in CIFAR-10, CIFAR-100, and SVHN. Moreover, the proposed theory provides a theoretical basis for a family of practically successful regularization methods in deep learning. We discuss several consequences of our results on one-shot learning, representation learning, deep learning, and curriculum learning. Unlike statistical learning theory, the proposed learning theory analyzes each problem instance individually via measure theory, rather than a set of problem instances via statistics. As a result, it provides different types of results and insights when compared to statistical learning theory.
2018-02-20
arXiv.org (prépublication)
dblp.uni-trier.de
Towards Understanding Generalization via Analytical Learning Theory
Kenji Kawaguchi
Yoshua Bengio
This paper introduces a novel measure-theoretic theory for machine learning that does not require statistical assumptions. Based on this the… (voir plus)ory, a new regularization method in deep learning is derived and shown to outperform previous methods in CIFAR-10, CIFAR-100, and SVHN. Moreover, the proposed theory provides a theoretical basis for a family of practically successful regularization methods in deep learning. We discuss several consequences of our results on one-shot learning, representation learning, deep learning, and curriculum learning. Unlike statistical learning theory, the proposed learning theory analyzes each problem instance individually via measure theory, rather than a set of problem instances via statistics. As a result, it provides different types of results and insights when compared to statistical learning theory.
2018-02-20
ArXiv (prépublication)
arxiv.org
arxiv.org
Boundary Seeking GANs
R Devon Hjelm
Athul Jacob
Adam Trischler
Gerry Che
Kyunghyun Cho
Yoshua Bengio
2018-02-14
International Conference on Learning Representations (publié)
openreview.net
openreview.net
Combining Model-based and Model-free RL via Multi-step Control Variates
Tong Che
Yuchen Lu
George Tucker
Surya Bhupatiraju
Shane Gu
Sergey Levine
Yoshua Bengio
2018-02-14
(publié)
openreview.net
openreview.net
Learning Generative Models with Locally Disentangled Latent Factors
Brady Neal
Alex Lamb
Sherjil Ozair
Devon Hjelm
Aaron Courville
Yoshua Bengio
Ioannis Mitliagkas
2018-02-14
(publié)
openreview.net
openreview.net
Finding Flatter Minima with SGD
Stanisław Jastrzębski
Zac Kenton
Devansh Arpit
Nicolas Ballas
Asja Fischer
Yoshua Bengio
Amos Storkey
2018-02-11
International Conference on Learning Representations (publié)
dblp.uni-trier.de
Graph Priors for Deep Neural Networks
Francis Dutil
Joseph Paul Cohen
Martin Weiss
Georgy Derevyanko
Yoshua Bengio
In this work we explore how gene-gene interaction graphs can be used as a prior for the representation of a model to construct features base… (voir plus)d on known interactions between genes. Most existing machine learning work on graphs focuses on building models when data is confined to a graph structure. In this work we focus on using the information from a graph to build better representations in our models. We use the percolate task, determining if a path exists across a grid for a set of node values, as a proxy for gene pathways. We create variants of the percolate task to explore where existing methods fail. We test the limits of existing methods in order to determine what can be improved when applying these methods to a real task. This leads us to propose new methods based on Graph Convolutional Networks (GCN) that use pooling and dropout to deal with noise in the graph prior.
2018-02-11
(publié)
openreview.net
openreview.net
SGD S MOOTHS THE S HARPEST D IRECTIONS
Stanisław Jastrzębski
Zac Kenton
Nicolas Ballas
Asja Fischer
Yoshua Bengio
Amos Storkey
Stochastic gradient descent (SGD) is able to find regions that generalize well, even in drastically over-parametrized models such as deep ne… (voir plus)ural networks. We observe that noise in SGD controls the spectral norm and conditioning of the Hessian throughout the training. We hypothesize the cause of this phenomenon is due to the dynamics of neurons saturating their non-linearity along the largest curvature directions, thus leading to improved conditioning.
2018-02-11
(publié)
openreview.net
openreview.net
Extending the Framework of Equilibrium Propagation to General Dynamics
Benjamin Scellier
Anirudh Goyal
Jonathan Binas
Thomas Mesnard
Yoshua Bengio
2018-02-10
International Conference on Learning Representations (publié)
openreview.net
openreview.net
A3T: Adversarially Augmented Adversarial Training
Akram Erraqabi
Aristide Baratin
Yoshua Bengio
Simon Lacoste-Julien
Recent research showed that deep neural networks are highly sensitive to so-called adversarial perturbations, which are tiny perturbations o… (voir plus)f the input data purposely designed to fool a machine learning classifier. Most classification models, including deep learning models, are highly vulnerable to adversarial attacks. In this work, we investigate a procedure to improve adversarial robustness of deep neural networks through enforcing representation invariance. The idea is to train the classifier jointly with a discriminator attached to one of its hidden layer and trained to filter the adversarial noise. We perform preliminary experiments to test the viability of the approach and to compare it to other standard adversarial training methods.
2018-01-11
ArXiv (prépublication)
arxiv.org
arxiv.org
Bayesian Model-Agnostic Meta-Learning
Taesup Kim
Jaesik Yoon
Ousmane Dia
Sungwoong Kim
Yoshua Bengio
Sungjin Ahn
Learning to infer Bayesian posterior from a few-shot dataset is an important step towards robust meta-learning due to the model uncertainty … (voir plus)inherent in the problem. In this paper, we propose a novel Bayesian model-agnostic meta-learning method. The proposed method combines scalable gradient-based meta-learning with nonparametric variational inference in a principled probabilistic framework. During fast adaptation, the method is capable of learning complex uncertainty structure beyond a point estimate or a simple Gaussian approximation. In addition, a robust Bayesian meta-update mechanism with a new meta-loss prevents overfitting during meta-update. Remaining an efficient gradient-based meta-learner, the method is also model-agnostic and simple to implement. Experiment results show the accuracy and robustness of the proposed method in various tasks: sinusoidal regression, image classification, active learning, and reinforcement learning.
2017-12-31
Advances in Neural Information Processing Systems 31 (NeurIPS 2018) (publié)
arxiv.org
arxiv.org
BigBrain: 1D convolutional neural networks for automated sementation of cortical layers
Konrad Wagstyl
Claude Lepage
Karl Zilles
Sebastian Bludau
G. Cucurul
Alan C. Evans
Paul C Fletcher
Adriana Romero
Joseph Paul Cohen
Stéphanie Larocque
Thomas Funck
Katrin Amunts
Yoshua Bengio
2017-12-31
(publié)
www.semanticscholar.org