Portrait de Yoshua Bengio

Yoshua Bengio

Membre académique principal

Chaire en IA Canada-CIFAR

Professeur titulaire, Université de Montréal

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

Biographie

*Pour toute demande média, veuillez écrire à medias@mila.quebec.

Pour plus d’information, contactez Cassidy MacNeil, adjointe principale et responsable des opérations cassidy.macneil@mila.quebec.

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

jamal.abouhaibeh@mila.quebec

Collaborateur·rice de recherche - Cambridge University

Superviseur⋅e principal⋅e :

berkes.anais@mila.quebec

Doctorat - UdeM

assouelr@mila.quebec

Doctorat - UdeM

xiaoyin.chen@mila.quebec

Collaborateur·rice de recherche - KAIST

sanghyeok.choi@mila.quebec

Aniket Didolkar

Doctorat - UdeM

aniket.didolkar@mila.quebec

Doctorat - UdeM

Co-superviseur⋅e :

Guillaume Lajoie

eric.elmoznino@mila.quebec

Doctorat - UdeM

lena-nehale.ezzine@mila.quebec

Jean-Pierre Falet

Doctorat - UdeM

jean-pierre.falet@mila.quebec

Doctorat

pietro.greiner@mila.quebec

Doctorat - UdeM

mohsin.hasan@mila.quebec

Doctorat - UdeM

moksh.jain@mila.quebec

Thomas Jiralerspong

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Guillaume Lajoie

thomas.jiralerspong@mila.quebec

Younesse Kaddar

Collaborateur·rice alumni - UdeM

younesse.kaddar@mila.quebec

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Alex Hernández-García

hyeonah.kim@mila.quebec

Collaborateur·rice de recherche - UdeM

minsu.kim@mila.quebec

Collaborateur·rice de recherche - KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY

jeongjae.lee@mila.quebec

Collaborateur·rice de recherche - s.o.

liusong299@gmail.com

Collaborateur·rice de recherche

liucheng@mila.quebec

Cristian Dragos Manta

Doctorat - UdeM

Co-superviseur⋅e :

cristian-dragos.manta@mila.quebec

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Guillaume Lajoie

mittalsa@mila.quebec

Visiteur de recherche indépendant - UdeM

nguyenph@mila.quebec

Collaborateur·rice de recherche - Ying Wu Coll of Computing

ali.parviz@mila.quebec

Collaborateur·rice de recherche - University of Waterloo

Superviseur⋅e principal⋅e :

elena.podina@mila.quebec

Jarrid Rector-Brooks

Doctorat - UdeM

jarrid.rector-brooks@mila.quebec

Postdoctorat - UdeM

danyal.rehman@mila.quebec

Postdoctorat - UdeM

oliver.richardson@mila.quebec

Camille Rochefort-Boulanger

Doctorat - UdeM

Superviseur⋅e principal⋅e :

rochefoc@mila.quebec

Postdoctorat - UdeM

Co-superviseur⋅e :

Alex Hernández-García

divya.sharma@mila.quebec

Mélisande Astrid Crystal Teng

Collaborateur·rice alumni - UdeM

Co-superviseur⋅e :

Hugo Larochelle

tengmeli@mila.quebec

Collaborateur·rice alumni - UdeM

alexander.tong@mila.quebec

Collaborateur·rice de recherche - University of Cambridge

oszkar.urban@mila.quebec

Collaborateur·rice alumni - UdeM

Co-superviseur⋅e :

donna.vakalis@mila.quebec

Siddarth Venkatraman

Doctorat - UdeM

Superviseur⋅e principal⋅e :

siddarth.venkatraman@mila.quebec

Collaborateur·rice de recherche

joseph.viviano2@mila.quebec

Esmeralda Whitammer

Collaborateur·rice de recherche - UdeM

esmeralda.whitammer@mila.quebec

Collaborateur·rice alumni

omar.younis@mila.quebec

Collaborateur·rice de recherche - UdeM

taeyoung.yun@mila.quebec

Doctorat - McGill

Superviseur⋅e principal⋅e :

Mathieu Blanchette

xi.zhang@mila.quebec

Doctorat - UdeM

tianyu.zhang@mila.quebec

Collaborateur·rice alumni - McGill

Superviseur⋅e principal⋅e :

zhaoming@mila.quebec

Billets de blogue

Generic thumbnail for Mila Blog articles.

22 février 2024

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

par

Mingde Harry Zhao

Safa Alver

Harm van Seijen

Romain Laroche

Scaling in the service of reasoning & model-based ML

4 avril 2023

Mise à l’échelle au service du raisonnement et de l’apprentissage automatique basé sur un modèle

par

A collaboration between Mila and Relation Therapeutics to discover novel synergistic combinations of drugs in vitro

23 mars 2022

Une collaboration entre Mila et Relation Therapeutics pour découvrir in vitro de nouvelles associations médicamenteuses synergiques

par

Jake P. Taylor-King

Generative Flow Networks

15 mars 2022

Les réseaux de flot génératifs

par

Publications

SPE: Symmetrical Prompt Enhancement for Factual Knowledge Retrieval

James M. Crawford

Matthew L. Ginsberg

Jacob Devlin

Ming-Wei Chang

Kenton Lee

Alex Graves

Abdel rahman Mohamed

Adi Haviv

Jonathan Berant

Amir Globerson

Chloe Kiddon

Pedro M. Domingos

Brian Lester

Rami Al-rfou'

Noah Constant. 2021

Weizhe Yuan … (voir 6 de plus)

Jinlan Fu

Zhengbao Jiang

Xiao Liu

Yanan Zheng

Zhengxiao Du

Ming Ding

Pretrained language models (PLMs) have 001 been shown to accumulate factual knowledge 002 from their unsupervised pretraining proce-003 dure… (voir plus)s (Petroni et al., 2019). Prompting is an 004 effective way to query such knowledge from 005 PLMs. Recently, continuous prompt methods 006 have been shown to have a larger potential 007 than discrete prompt methods in generating ef-008 fective queries (Liu et al., 2021a). However, 009 these methods do not consider symmetry of 010 the task. In this work, we propose Symmet-011 rical Prompt Enhancement (SPE), a continu-012 ous prompt-based method for fact retrieval that 013 leverages the symmetry of the task. Our results 014 on LAMA, a popular fact retrieval dataset, 015 show signiﬁcant improvement of SPE over pre-016 vious prompt methods

2020-12-31

(publié)

www.semanticscholar.org

Tackling Situated Multi-Modal Task-Oriented Dialogs with a Single Transformer Model

−. i.eUT

R´ejean Ducharme

P Vincent

Morgan Kaufmann

Yen-Chun Chen

Linjie Li

Licheng Yu

Matthew Henderson

Blaise Thomson

Ehsan Hosseini-Asl

Bryan McCann

Chien-Sheng Wu

Samuel Humeau

Kurt Shuster

Marie-Anne Lachaux

The Situated Interactive Multi-Modal Conver-001 sations (SIMMC) 2.0 aims to create virtual 002 shopping assistants that can accept complex 0… (voir plus)03 multi-modal inputs, i.e. visual appearances of 004 objects and user utterances. It consists of four 005 subtasks, multi-modal disambiguation (MM-006 Disamb), multi-modal coreference resolution 007 (MM-Coref), multi-modal dialog state tracking 008 (MM-DST), and response retrieval and genera-009 tion. While many task-oriented dialog systems 010 usually tackle each subtask separately, we pro-011 pose a jointly learned encoder-decoder that per-012 forms all four subtasks at once for efficiency. 013 Moreover, we handle the multi-modality of the 014 challenge by representing visual objects as spe-015 cial tokens whose joint embedding is learned 016 via auxiliary tasks. This approach won the MM-017 Coref and response retrieval subtasks and nom-018 inated runner-up for the remaining subtasks 019 using a single unified model. In particular, 020 our model achieved 81.5% MRR, 71.2% R@1, 021 95.0% R@5, 98.2% R@10, and 1.9 mean rank 022 in response retrieval task, setting a high bar for 023 the state-of-the-art result in the SIMMC 2.0 024 track of the Dialog Systems Technology Chal-025 lenge 10 (DSTC10). 026

2020-12-31

(publié)

www.semanticscholar.org

Test Sample Accuracy Scales with Training Sample Density in Neural Networks

Andrea Vedaldi

Balaji Lakshminarayanan

Intuitively, one would expect accuracy of a trained neural network's prediction on test samples to correlate with how densely the samples ar… (voir plus)e surrounded by seen training samples in representation space. We find that a bound on empirical training error smoothed across linear activation regions scales inversely with training sample density in representation space. Empirically, we verify this bound is a strong predictor of the inaccuracy of the network's prediction on test samples. For unseen test sets, including those with out-of-distribution samples, ranking test samples by their local region's error bound and discarding samples with the highest bounds raises prediction accuracy by up to 20% in absolute terms for image classification datasets, on average over thresholds.

2020-12-31

arXiv.org (prépublication)

Unifying Likelihood-free Inference with Black-box Sequence Design and Beyond

Aaron Courville

2020-12-31

arXiv.org (prépublication)

dblp.uni-trier.de

Using Artificial Intelligence to Visualize the Impacts of Climate Change

Alexandra Luccioni

Public awareness and concern about climate change often do not match the magnitude of its threat to humans and our environment. One reason f… (voir plus)or this disagreement is that it is difficult to mentally simulate the effects of a process as complex as climate change and to have a concrete representation of the impact that our individual actions will have on our own future, especially if the consequences are long term and abstract. To overcome these challenges, we propose to use cutting-edge artificial intelligence (AI) approaches to develop an interactive personalized visualization tool, the AI climate impact visualizer. It will allow a user to enter an address—be it their house, their school, or their workplace—-and it will provide them with an AI-imagined possible visualization of the future of this location in 2050 following the detrimental effects of climate change such as floods, storms, and wildfires. This image will be accompanied by accessible information regarding the science behind climate change, i.e., why extreme weather events are becoming more frequent and what kinds of changes are happening on a local and global scale.

2020-12-31

IEEE Computer Graphics and Applications (publié)

What Makes Machine Reading Comprehension Questions Difﬁcult? Investigating Variation in Passage Sources and Question Types

Susan Bartlett

Grzegorz Kondrak

Max Bartolo

Alastair Roberts

Johannes Welbl

Steven Bird

Ewan Klein

Edward Loper

Samuel R. Bowman

George Dahl. 2021

What

Chao Pang

Junyuan Shang

Jiaxiang Liu

Xuyi Chen

Yanbin Zhao

Yuxiang Lu

Weixin Liu

Zhi-901 hua Wu

Weibao Gong … (voir 21 de plus)

Jianzhong Liang

Zhizhou Shang

Peng Sun

Ouyang Xuan

Dianhai

Houwen Tian

Hua Wu

Haifeng Wang

Adam Trischler

Tong Wang

Xingdi Yuan

Justin Har-908

Alessandro Sordoni

Philip Bachman

Adina Williams

Nikita Nangia

Zhilin Yang

Peng Qi

ing. In

For a natural language understanding bench-001 mark to be useful in research, it has to con-002 sist of examples that are diverse and difﬁ… (voir plus)-003 cult enough to discriminate among current and 004 near-future state-of-the-art systems. However, 005 we do not yet know how best to select pas-006 sages to collect a variety of challenging exam-007 ples. In this study, we crowdsource multiple-008 choice reading comprehension questions for 009 passages taken from seven qualitatively dis-010 tinct sources, analyzing what attributes of pas-011 sages contribute to the difﬁculty and question 012 types of the collected examples. To our sur-013 prise, we ﬁnd that passage source, length, and 014 readability measures do not signiﬁcantly affect 015 question difﬁculty. Through our manual anno-016 tation of seven reasoning types, we observe 017 several trends between passage sources and 018 reasoning types, e.g., logical reasoning is more 019 often required in questions written for techni-020 cal passages. These results suggest that when 021 creating a new benchmark dataset, selecting a 022 diverse set of passages can help ensure a di-023 verse range of question types, but that passage 024 difﬁculty need not be a priority. 025

2020-12-31

(publié)

www.semanticscholar.org

Machine Learning for Glacier Monitoring in the Hindu Kush Himalaya

Benjamin Akera

Bibek Aryal

Tenzing Chogyal Sherpa

Finu Shresta

Anthony Ortiz

Juan Lavista Ferres

M. Matin

2020-12-08

ArXiv (prépublication)

Hybrid Models for Learning to Branch

Prateek Gupta

Maxime Gasse

Elias B. Khalil

M. Pawan Kumar

Andrea Lodi

A recent Graph Neural Network (GNN) approach for learning to branch has been shown to successfully reduce the running time of branch-and-bou… (voir plus)nd algorithms for Mixed Integer Linear Programming (MILP). While the GNN relies on a GPU for inference, MILP solvers are purely CPU-based. This severely limits its application as many practitioners may not have access to high-end GPUs. In this work, we ask two key questions. First, in a more realistic setting where only a CPU is available, is the GNN model still competitive? Second, can we devise an alternate computationally inexpensive model that retains the predictive power of the GNN architecture? We answer the first question in the negative, and address the second question by proposing a new hybrid architecture for efficient branching on CPU machines. The proposed architecture combines the expressive power of GNNs with computationally inexpensive multi-layer perceptrons (MLP) for branching. We evaluate our methods on four classes of MILP problems, and show that they lead to up to 26% reduction in solver running time compared to state-of-the-art methods without a GPU, while extrapolating to harder problems than it was trained on. The code for this project is publicly available at https://github.com/pg2455/Hybrid-learn2branch.

2020-12-06

Conference on Neural Information Processing Systems (Accept (poster))

Untangling tradeoffs between recurrence and self-attention in neural networks

Bhargav Kanuparthi

Guillaume Lajoie

2020-12-06

Advances in Neural Information Processing Systems (Accept (poster))

Your GAN is Secretly an Energy-based Model and You Should Use Discriminator Driven Latent Sampling

Tong Che

Jascha Sohl-Dickstein

Hugo Larochelle

Yuan Cao

We show that the sum of the implicit generator log-density …

2020-12-06

Conference on Neural Information Processing Systems (Accept (poster))

Establishing an evaluation metric to quantify climate change image realism

Sharon Zhou

Alexandra Luccioni

Gautier Cosne

Michael S. Bernstein

2020-11-30

Informatica: Journal of Applied Machines Electrical Electronics Computer Science and Communication Systems (publié)

RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design

Cheng-Hao Liu

Maksym Korablyov

Stanisław Jastrzębski

Paweł Włodarczyk-Pruszyński

Marwin H. S. Segler

De novo molecule generation often results in chemically unfeasible molecules. A natural idea to mitigate this problem is to bias the search … (voir plus)process towards more easily synthesizable molecules using a proxy for synthetic accessibility. However, using currently available proxies still results in highly unrealistic compounds. We investigate the feasibility of training deep graph neural networks to approximate the outputs of a retrosynthesis planning software, and their use to bias the search process. We evaluate our method on a benchmark involving searching for drug-like molecules with antibiotic properties. Compared to enumerating over five million existing molecules from the ZINC database, our approach finds molecules predicted to be more likely to be antibiotics while maintaining good drug-like properties and being easily synthesizable. Importantly, our deep neural network can successfully filter out hard to synthesize molecules while achieving a

2020-11-24

arXiv (prépublication)