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

Berkes Anaïs

Collaborateur·rice de recherche - Cambridge University

Superviseur⋅e principal⋅e :

Rim Assouel

Doctorat - UdeM

Stefan Bauer

Visiteur de recherche indépendant

Co-superviseur⋅e :

Guillaume Lajoie

Shahana Chatterjee

Collaborateur·rice de recherche - N/A

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Collaborateur·rice de recherche - KAIST

Doctorat - UdeM

Collaborateur·rice alumni - UdeM

Co-superviseur⋅e :

Loubna Benabbou

Desmond Elliott

Visiteur de recherche indépendant

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Doctorat - UdeM

Doctorat

Doctorat - UdeM

Moksh Jain

Doctorat - UdeM

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Collaborateur·rice alumni - UdeM

Hyeonah Kim

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Alex Hernandez-Garcia

Tabitha Edith Lee

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Collaborateur·rice alumni

Collaborateur·rice alumni - UdeM

Cristian Dragos Manta

Doctorat - UdeM

Co-superviseur⋅e :

Dhanya Sridhar

Sarthak Mittal

Doctorat - 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

Collaborateur·rice de recherche - University of Waterloo

Superviseur⋅e principal⋅e :

David Rolnick

Camille Rochefort-Boulanger

Nassim Rahaman

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

Amine RAZIG

Collaborateur·rice de recherche - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Postdoctorat - UdeM

Postdoctorat - UdeM

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Julie Hussin

Dragos Secrieru

Collaborateur·rice alumni - UdeM

Divya Sharma

Postdoctorat

Co-superviseur⋅e :

Alex Hernandez-Garcia

Mélisande Astrid Crystal Teng

Vincent Taboga

Collaborateur·rice alumni - Polytechnique

Co-superviseur⋅e :

Doctorat - UdeM

Co-superviseur⋅e :

Hugo Larochelle

Ivan Titov

Collaborateur·rice de recherche

Superviseur⋅e principal⋅e :

Siva Reddy

Alex Tong

Collaborateur·rice alumni - UdeM

Collaborateur·rice alumni - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Collaborateur·rice de recherche

Collaborateur·rice de recherche - UdeM

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

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

Introduction to NIPS 2017 Competition Track

Sergio Escalera

Markus Weimer

Mikhail Burtsev

Valentin Malykh

Varvara Logacheva

Ryan Lowe

Iulian V. Serban

Alexander Rudnicky

Alan W. Black

Shrimai Prabhumoye

Łukasz Kidziński

Sharada Prasanna Mohanty

Carmichael F. Ong

Jennifer L. Hicks

Sergey Levine

Marcel Salathé

Scott Delp

Iker Huerga

Alexander Grigorenko … (voir 19 de plus)

Leifur Thorbergsson

Anasuya Das

Kyla Nemitz

Jenna Sandker

Stephen King

Alexander S. Ecker

Leon A. Gatys

Matthias Bethge

Jordan Boyd-Graber

Shi Feng

Pedro Rodriguez

Mohit Iyyer

He He

Hal Daumé III

Sean McGregor

Amir Banifatemi

Alexey Kurakin

Ian G Goodfellow

Samy Bengio

2018-09-27

The NIPS '17 Competition: Building Intelligent Systems (publié)

doi.org

The First Conversational Intelligence Challenge

Mikhail Burtsev

Varvara Logacheva

Valentin Malykh

Iulian V. Serban

Ryan Lowe

Shrimai Prabhumoye

Alan W. Black

Alexander Rudnicky

2018-09-27

The NIPS '17 Competition: Building Intelligent Systems (publié)

doi.org

Convergence Properties of Deep Neural Networks on Separable Data

Remi Tachet des Combes

Mohammad Pezeshki

Samira Shabanian

Aaron Courville

While a lot of progress has been made in recent years, the dynamics of learning in deep nonlinear neural networks remain to this day largely… (voir plus) misunderstood. In this work, we study the case of binary classification and prove various properties of learning in such networks under strong assumptions such as linear separability of the data. Extending existing results from the linear case, we confirm empirical observations by proving that the classification error also follows a sigmoidal shape in nonlinear architectures. We show that given proper initialization, learning expounds parallel independent modes and that certain regions of parameter space might lead to failed training. We also demonstrate that input norm and features’ frequency in the dataset lead to distinct convergence speeds which might shed some light on the generalization capabilities of deep neural networks. We provide a comparison between the dynamics of learning with cross-entropy and hinge losses, which could prove useful to understand recent progress in the training of generative adversarial networks. Finally, we identify a phenomenon that we baptize gradient starvation where the most frequent features in a dataset prevent the learning of other less frequent but equally informative features.

2018-09-26

(publié)

Deep Graph Infomax

Petar Veličković

William Fedus

William L. Hamilton

Pietro Lio

R Devon Hjelm

We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised ma… (voir plus)nner. DGI relies on maximizing mutual information between patch representations and corresponding high-level summaries of graphs---both derived using established graph convolutional network architectures. The learnt patch representations summarize subgraphs centered around nodes of interest, and can thus be reused for downstream node-wise learning tasks. In contrast to most prior approaches to unsupervised learning with GCNs, DGI does not rely on random walk objectives, and is readily applicable to both transductive and inductive learning setups. We demonstrate competitive performance on a variety of node classification benchmarks, which at times even exceeds the performance of supervised learning.

2018-09-26

ArXiv (prépublication)

DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation

Rim Assouel

Mohamed Ahmed

Marwin Segler

Amir Saffari

Generating novel molecules with optimal properties is a crucial step in many industries such as drug discovery. Recently, deep generative mo… (voir plus)dels have shown a promising way of performing de-novo molecular design. Although graph generative models are currently available they either have a graph size dependency in their number of parameters, limiting their use to only very small graphs or are formulated as a sequence of discrete actions needed to construct a graph, making the output graph non-differentiable w.r.t the model parameters, therefore preventing them to be used in scenarios such as conditional graph generation. In this work we propose a model for conditional graph generation that is computationally efficient and enables direct optimisation of the graph. We demonstrate favourable performance of our model on prototype-based molecular graph conditional generation tasks.

2018-09-26

ArXiv (prépublication)

EnGAN: Latent Space MCMC and Maximum Entropy Generators for Energy-based Models

2018-09-26

(publié)

Learning powerful policies and better dynamics models by encouraging consistency

2018-09-26

(publié)

Learning of Sophisticated Curriculums by viewing them as Graphs over Tasks

Lucas Willems

2018-09-26

(publié)

Modeling the Long Term Future in Model-Based Reinforcement Learning

Nan Rosemary Ke

Amanpreet Singh

Ahmed Touati

Anirudh Goyal

Devi Parikh

Dhruv Batra

2018-09-26

International Conference on Learning Representations (publié)

Probabilistic Planning with Sequential Monte Carlo Methods

Alexandre Piché

Valentin Thomas

Cyril Ibrahim

Chris Pal

2018-09-26

International Conference on Learning Representations (publié)

Reinforced Imitation Learning from Observations

Pedro O. Pinheiro

2018-09-26

(publié)

Towards the Latent Transcriptome

Assya Trofimov

Francis Dutil

Claude Perreault

S. Lemieux

Joseph Paul Cohen

In this work we propose a method to compute continuous embeddings for kmers from raw RNA-seq data, in a reference-free fashion. We report th… (voir plus)at our model captures information of both DNA sequence similarity as well as DNA sequence abundance in the embedding latent space. We confirm the quality of these vectors by comparing them to known gene sub-structures and report that the latent space recovers exon information from raw RNA-Seq data from acute myeloid leukemia patients. Furthermore we show that this latent space allows the detection of genomic abnormalities such as translocations as well as patient-specific mutations, making this representation space both useful for visualization as well as analysis.

2018-09-26

ArXiv (prépublication)