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

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

Minsu Kim

Stagiaire de recherche - UdeM

Hyeonah Kim

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Alex Hernandez

Yaroslav KIVVA

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

Doctorat - UdeM

Doctorat - McGill

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Aaron Courville

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

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

Modularity Matters: Learning Invariant Relational Reasoning Tasks

Jason Jo

Vikas Verma

We focus on two supervised visual reasoning tasks whose labels encode a semantic relational rule between two or more objects in an image: th… (voir plus)e MNIST Parity task and the colorized Pentomino task. The objects in the images undergo random translation, scaling, rotation and coloring transformations. Thus these tasks involve invariant relational reasoning. We report uneven performance of various deep CNN models on these two tasks. For the MNIST Parity task, we report that the VGG19 model soundly outperforms a family of ResNet models. Moreover, the family of ResNet models exhibits a general sensitivity to random initialization for the MNIST Parity task. For the colorized Pentomino task, now both the VGG19 and ResNet models exhibit sluggish optimization and very poor test generalization, hovering around 30% test error. The CNN we tested all learn hierarchies of fully distributed features and thus encode the distributed representation prior. We are motivated by a hypothesis from cognitive neuroscience which posits that the human visual cortex is modularized, and this allows the visual cortex to learn higher order invariances. To this end, we consider a modularized variant of the ResNet model, referred to as a Residual Mixture Network (ResMixNet) which employs a mixture-of-experts architecture to interleave distributed representations with more specialized, modular representations. We show that very shallow ResMixNets are capable of learning each of the two tasks well, attaining less than 2% and 1% test error on the MNIST Parity and the colorized Pentomino tasks respectively. Most importantly, the ResMixNet models are extremely parameter efficient: generalizing better than various non-modular CNNs that have over 10x the number of parameters. These experimental results support the hypothesis that modularity is a robust prior for learning invariant relational reasoning.

2018-06-18

ArXiv (prépublication)

On the Iterative Refinement of Densely Connected Representation Levels for Semantic Segmentation

Arantxa Casanova

Guillem Cucurull

Michal Drozdzal

Adriana Romero Soriano

State-of-the-art semantic segmentation approaches increase the receptive field of their models by using either a downsampling path composed … (voir plus)of poolings/strided convolutions or successive dilated convolutions. However, it is not clear which operation leads to best results. In this paper, we systematically study the differences introduced by distinct receptive field enlargement methods and their impact on the performance of a novel architecture, called Fully Convolutional DenseResNet (FC-DRN). FC-DRN has a densely connected backbone composed of residual networks. Following standard image segmentation architectures, receptive field enlargement operations that change the representation level are interleaved among residual networks. This allows the model to exploit the benefits of both residual and dense connectivity patterns, namely: gradient flow, iterative refinement of representations, multi-scale feature combination and deep supervision. In order to highlight the potential of our model, we test it on the challenging CamVid urban scene understanding benchmark and make the following observations: 1) downsampling operations outperform dilations when the model is trained from scratch, 2) dilations are useful during the finetuning step of the model, 3) coarser representations require less refinement steps, and 4) ResNets (by model construction) are good regularizers, since they can reduce the model capacity when needed. Finally, we compare our architecture to alternative methods and report state-of-the-art result on the Camvid dataset, with at least twice fewer parameters.

2018-06-18

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publié)

doi.org

Towards Gene Expression Convolutions using Gene Interaction Graphs

Francis Dutil

Joseph Paul Cohen

Martin Weiss

Georgy Derevyanko

We study the challenges of applying deep learning to gene expression data. We find experimentally that there exists non-linear signal in the… (voir plus) data, however is it not discovered automatically given the noise and low numbers of samples used in most research. We discuss how gene interaction graphs (same pathway, protein-protein, co-expression, or research paper text association) can be used to impose a bias on a deep model similar to the spatial bias imposed by convolutions on an image. We explore the usage of Graph Convolutional Neural Networks coupled with dropout and gene embeddings to utilize the graph information. We find this approach provides an advantage for particular tasks in a low data regime but is very dependent on the quality of the graph used. We conclude that more work should be done in this direction. We design experiments that show why existing methods fail to capture signal that is present in the data when features are added which clearly isolates the problem that needs to be addressed.

2018-06-18

ArXiv (prépublication)

Manifold Mixup: Encouraging Meaningful On-Manifold Interpolation as a Regularizer

Vikas Verma

Alex Lamb

Christopher Beckham

Aaron Courville

Ioannis Mitliagkas

Deep networks often perform well on the data manifold on which they are trained, yet give incorrect (and often very confident) answers when … (voir plus)evaluated on points from off of the training distribution. This is exemplified by the adversarial examples phenomenon but can also be seen in terms of model generalization and domain shift. We propose Manifold Mixup which encourages the network to produce more reasonable and less confident predictions at points with combinations of attributes not seen in the training set. This is accomplished by training on convex combinations of the hidden state representations of data samples. Using this method, we demonstrate improved semi-supervised learning, learning with limited labeled data, and robustness to adversarial examples. Manifold Mixup requires no (significant) additional computation. Analytical experiments on both real data and synthetic data directly support our hypothesis for why the Manifold Mixup method improves results.

2018-06-13

arXiv.org (prépublication)

dblp.uni-trier.de

Learning to rank for censored survival data

Margaux Luck

Tristan Sylvain

Joseph Paul Cohen

Heloise Cardinal

Andrea Lodi

Survival analysis is a type of semi-supervised ranking task where the target output (the survival time) is often right-censored. Utilizing t… (voir plus)his information is a challenge because it is not obvious how to correctly incorporate these censored examples into a model. We study how three categories of loss functions, namely partial likelihood methods, rank methods, and our classification method based on a Wasserstein metric (WM) and the non-parametric Kaplan Meier estimate of the probability density to impute the labels of censored examples, can take advantage of this information. The proposed method allows us to have a model that predict the probability distribution of an event. If a clinician had access to the detailed probability of an event over time this would help in treatment planning. For example, determining if the risk of kidney graft rejection is constant or peaked after some time. Also, we demonstrate that this approach directly optimizes the expected C-index which is the most common evaluation metric for ranking survival models.

2018-06-06

ArXiv (prépublication)

Commonsense mining as knowledge base completion? A study on the impact of novelty

Stanisław Jastrzębski

Dzmitry Bahdanau

Seyedarian Hosseini

Michael Noukhovitch

Jackie Cheung

Commonsense knowledge bases such as ConceptNet represent knowledge in the form of relational triples. Inspired by recent work by Li et al., … (voir plus)we analyse if knowledge base completion models can be used to mine commonsense knowledge from raw text. We propose novelty of predicted triples with respect to the training set as an important factor in interpreting results. We critically analyse the difficulty of mining novel commonsense knowledge, and show that a simple baseline method that outperforms the previous state of the art on predicting more novel triples.

2018-06-01

Proceedings of the Workshop on Generalization in the Age of Deep Learning (publié)

doi.org

Low-memory convolutional neural networks through incremental depth-first processing

Jonathan Binas

We introduce an incremental processing scheme for convolutional neural network (CNN) inference, targeted at embedded applications with limit… (voir plus)ed memory budgets. Instead of processing layers one by one, individual input pixels are propagated through all parts of the network they can influence under the given structural constraints. This depth-first updating scheme comes with hard bounds on the memory footprint: the memory required is constant in the case of 1D input and proportional to the square root of the input dimension in the case of 2D input.

2018-04-28

ArXiv (prépublication)

Towards End-to-end Spoken Language Understanding

Dmitriy Serdyuk

Yongqiang Wang

Christian Fuegen

Anuj Kumar

Baiyang Liu

Spoken language understanding system is traditionally designed as a pipeline of a number of components. First, the audio signal is processed… (voir plus) by an automatic speech recognizer for transcription or n-best hypotheses. With the recognition results, a natural language understanding system classifies the text to structured data as domain, intent and slots for down-streaming consumers, such as dialog system, hands-free applications. These components are usually developed and optimized independently. In this paper, we present our study on an end-to-end learning system for spoken language understanding. With this unified approach, we can infer the semantic meaning directly from audio features without the intermediate text representation. This study showed that the trained model can achieve reasonable good result and demonstrated that the model can capture the semantic attention directly from the audio features.

2018-04-15

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (publié)

doi.org

Learning Anonymized Representations with Adversarial Neural Networks

Clément Feutry

Pablo Piantanida

P. Duhamel

Statistical methods protecting sensitive information or the identity of the data owner have become critical to ensure privacy of individuals… (voir plus) as well as of organizations. This paper investigates anonymization methods based on representation learning and deep neural networks, and motivated by novel information theoretical bounds. We introduce a novel training objective for simultaneously training a predictor over target variables of interest (the regular labels) while preventing an intermediate representation to be predictive of the private labels. The architecture is based on three sub-networks: one going from input to representation, one from representation to predicted regular labels, and one from representation to predicted private labels. The training procedure aims at learning representations that preserve the relevant part of the information (about regular labels) while dismissing information about the private labels which correspond to the identity of a person. We demonstrate the success of this approach for two distinct classification versus anonymization tasks (handwritten digits and sentiment analysis).

2018-02-26

ArXiv (prépublication)

Generalization in Machine Learning via Analytical Learning Theory

Kenji Kawaguchi

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.

2018-02-21

arXiv.org (prépublication)

dblp.uni-trier.de

Towards Understanding Generalization via Analytical Learning Theory

Kenji Kawaguchi

2018-02-21

ArXiv (prépublication)

Boundary Seeking GANs

(Rex) Devon Hjelm

Athul Jacob

Adam Trischler

Gerry Che

Kyunghyun Cho