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

Collaborateur·rice de recherche - UdeM

Michał Koziarski

Collaborateur·rice alumni - UdeM

Salem Lahlou

Collaborateur·rice alumni - UdeM

Seanie Lee

Collaborateur·rice alumni - UdeM

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Zhen Liu

Collaborateur·rice alumni - UdeM

Superviseur⋅e principal⋅e :

Liam Paull

Chenghao Liu

Collaborateur·rice alumni

Collaborateur·rice alumni - Imperial College London

Doctorat - UdeM

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

Dounia Shaaban Kabakibo

Stagiaire de recherche - 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

Speech Model Pre-training for End-to-End Spoken Language Understanding

Loren Lugosch

Mirco Ravanelli

Patrick Ignoto

Vikrant Singh Tomar

Whereas conventional spoken language understanding (SLU) systems map speech to text, and then text to intent, end-to-end SLU systems map spe… (voir plus)ech directly to intent through a single trainable model. Achieving high accuracy with these end-to-end models without a large amount of training data is difficult. We propose a method to reduce the data requirements of end-to-end SLU in which the model is first pre-trained to predict words and phonemes, thus learning good features for SLU. We introduce a new SLU dataset, Fluent Speech Commands, and show that our method improves performance both when the full dataset is used for training and when only a small subset is used. We also describe preliminary experiments to gauge the model's ability to generalize to new phrases not heard during training.

2019-09-15

Interspeech 2019 (publié)

Torchmeta: A Meta-Learning library for PyTorch

Tristan Deleu

Tobias Würfl

Mandana Samiei

Joseph Paul Cohen

The constant introduction of standardized benchmarks in the literature has helped accelerating the recent advances in meta-learning research… (voir plus). They offer a way to get a fair comparison between different algorithms, and the wide range of datasets available allows full control over the complexity of this evaluation. However, for a large majority of code available online, the data pipeline is often specific to one dataset, and testing on another dataset requires significant rework. We introduce Torchmeta, a library built on top of PyTorch that enables seamless and consistent evaluation of meta-learning algorithms on multiple datasets, by providing data-loaders for most of the standard benchmarks in few-shot classification and regression, with a new meta-dataset abstraction. It also features some extensions for PyTorch to simplify the development of models compatible with meta-learning algorithms. The code is available here: this https URL

2019-09-14

ArXiv (prépublication)

The effect of task and training on intermediate representations in convolutional neural networks revealed with modified RV similarity analysis

Jessica A.F. Thompson

Marc Schoenwiesner

2019-09-13

2019 Conference on Cognitive Computational Neuroscience (publié)

Data-Driven Approach to Encoding and Decoding 3-D Crystal Structures

Jordan Hoffmann

Louis Maestrati

Yoshihide Sawada

Jian Tang

Jean Michel Sellier

Generative models have achieved impressive results in many domains including image and text generation. In the natural sciences, generative … (voir plus)models have led to rapid progress in automated drug discovery. Many of the current methods focus on either 1-D or 2-D representations of typically small, drug-like molecules. However, many molecules require 3-D descriptors and exceed the chemical complexity of commonly used dataset. We present a method to encode and decode the position of atoms in 3-D molecules from a dataset of nearly 50,000 stable crystal unit cells that vary from containing 1 to over 100 atoms. We construct a smooth and continuous 3-D density representation of each crystal based on the positions of different atoms. Two different neural networks were trained on a dataset of over 120,000 three-dimensional samples of single and repeating crystal structures, made by rotating the single unit cells. The first, an Encoder-Decoder pair, constructs a compressed latent space representation of each molecule and then decodes this description into an accurate reconstruction of the input. The second network segments the resulting output into atoms and assigns each atom an atomic number. By generating compressed, continuous latent spaces representations of molecules we are able to decode random samples, interpolate between two molecules, and alter known molecules.

2019-09-03

ArXiv (prépublication)

openreview.net

Interpolation Consistency Training for Semi-Supervised Learning

Vikas Verma

Kenji Kawaguchi

Alex Lamb

Juho Kannala

David Lopez-Paz

Arno Solin

2019-08-10

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (publié)

Combined Reinforcement Learning via Abstract Representations

Vincent Francois-Lavet

Doina Precup

Joelle Pineau

In the quest for efficient and robust reinforcement learning methods, both model-free and model-based approaches offer advantages. In this p… (voir plus)aper we propose a new way of explicitly bridging both approaches via a shared low-dimensional learned encoding of the environment, meant to capture summarizing abstractions. We show that the modularity brought by this approach leads to good generalization while being computationally efficient, with planning happening in a smaller latent state space. In addition, this approach recovers a sufficient low-dimensional representation of the environment, which opens up new strategies for interpretable AI, exploration and transfer learning.

2019-07-17

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

Towards Non-saturating Recurrent Units for Modelling Long-term Dependencies

Sarath Chandar

Chinnadhurai Sankar

Eugene Vorontsov

Samira Ebrahimi Kahou

Modelling long-term dependencies is a challenge for recurrent neural networks. This is primarily due to the fact that gradients vanish durin… (voir plus)g training, as the sequence length increases. Gradients can be attenuated by transition operators and are attenuated or dropped by activation functions. Canonical architectures like LSTM alleviate this issue by skipping information through a memory mechanism. We propose a new recurrent architecture (Non-saturating Recurrent Unit; NRU) that relies on a memory mechanism but forgoes both saturating activation functions and saturating gates, in order to further alleviate vanishing gradients. In a series of synthetic and real world tasks, we demonstrate that the proposed model is the only model that performs among the top 2 models across all tasks with and without long-term dependencies, when compared against a range of other architectures.

2019-07-17

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

Do Neural Dialog Systems Use the Conversation History Effectively? An Empirical Study

Chinnadhurai Sankar

Sandeep Subramanian

Chris Pal

Sarath Chandar

Neural generative models have been become increasingly popular when building conversational agents. They offer flexibility, can be easily ad… (voir plus)apted to new domains, and require minimal domain engineering. A common criticism of these systems is that they seldom understand or use the available dialog history effectively. In this paper, we take an empirical approach to understanding how these models use the available dialog history by studying the sensitivity of the models to artificially introduced unnatural changes or perturbations to their context at test time. We experiment with 10 different types of perturbations on 4 multi-turn dialog datasets and find that commonly used neural dialog architectures like recurrent and transformer-based seq2seq models are rarely sensitive to most perturbations such as missing or reordering utterances, shuffling words, etc. Also, by open-sourcing our code, we believe that it will serve as a useful diagnostic tool for evaluating dialog systems in the future.

2019-07-01

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (publié)

On the interplay between noise and curvature and its effect on optimization and generalization

Valentin Thomas

Fabian Pedregosa

Bart van Merriënboer

Pierre-Antoine Mangazol

Nicolas Le Roux

The speed at which one can minimize an expected loss using stochastic methods depends on two properties: the curvature of the loss and the v… (voir plus)ariance of the gradients. While most previous works focus on one or the other of these properties, we explore how their interaction affects optimization speed. Further, as the ultimate goal is good generalization performance, we clarify how both curvature and noise are relevant to properly estimate the generalization gap. Realizing that the limitations of some existing works stems from a confusion between these matrices, we also clarify the distinction between the Fisher matrix, the Hessian, and the covariance matrix of the gradients.

2019-06-19

ArXiv (prépublication)

Information matrices and generalization

Valentin Thomas

Fabian Pedregosa

Bart van Merriënboer

Pierre-Antoine Manzagol

Nicolas Le Roux

This work revisits the use of information criteria to characterize the generalization of deep learning models. In particular, we empirically… (voir plus) demonstrate the effectiveness of the Takeuchi information criterion (TIC), an extension of the Akaike information criterion (AIC) for misspecified models, in estimating the generalization gap, shedding light on why quantities such as the number of parameters cannot quantify generalization. The TIC depends on both the Hessian of the loss H and the covariance of the gradients C. By exploring the similarities and differences between these two matrices as well as the Fisher information matrix F, we study the interplay between noise and curvature in deep models. We also address the question of whether C is a reasonable approximation to F, as is commonly assumed.

2019-06-18

arXiv.org (preprint)

dblp.uni-trier.de

Information matrices and generalization

Valentin Thomas

Fabian Pedregosa

Bart van Merriënboer

Pierre-Antoine Manzagol

Nicolas Le Roux

2019-06-18

arXiv.org (prépublication)

dblp.uni-trier.de

Conditional Computation for Continual Learning

Min Lin

Jie Fu

Catastrophic forgetting of connectionist neural networks is caused by the global sharing of parameters among all training examples. In this … (voir plus)study, we analyze parameter sharing under the conditional computation framework where the parameters of a neural network are conditioned on each input example. At one extreme, if each input example uses a disjoint set of parameters, there is no sharing of parameters thus no catastrophic forgetting. At the other extreme, if the parameters are the same for every example, it reduces to the conventional neural network. We then introduce a clipped version of maxout networks which lies in the middle, i.e. parameters are shared partially among examples. Based on the parameter sharing analysis, we can locate a limited set of examples that are interfered when learning a new example. We propose to perform rehearsal on this set to prevent forgetting, which is termed as conditional rehearsal. Finally, we demonstrate the effectiveness of the proposed method in an online non-stationary setup, where updates are made after each new example and the distribution of the received example shifts over time.

2019-06-16

ArXiv (prépublication)