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

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

Paul Bertin

Doctorat - UdeM

Joyce Chai

Visiteur de recherche indépendant

Superviseur⋅e principal⋅e :

Siva Reddy

Shahana Chatterjee

Collaborateur·rice de recherche - N/A

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Collaborateur·rice de recherche - KAIST

Collaborateur·rice alumni - UdeM

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

Leo Feng

Doctorat - UdeM

Doctorat

Doctorat - UdeM

Edward Hu

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

Salem Lahlou

Collaborateur·rice alumni - UdeM

Tabitha Edith Lee

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Zhen Liu

Collaborateur·rice alumni - UdeM

Superviseur⋅e principal⋅e :

Liam Paull

Chenghao Liu

Collaborateur·rice alumni

Doctorat - UdeM

Collaborateur·rice alumni - UdeM

Cristian Dragos Manta

Doctorat - UdeM

Co-superviseur⋅e :

Dhanya Sridhar

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

Collaborateur·rice de recherche - University of Waterloo

Superviseur⋅e principal⋅e :

David Rolnick

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

Visiteur de recherche indépendant - UdeM

Oli RICHARDSON

Postdoctorat - UdeM

Camille Rochefort-Boulanger

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Julie Hussin

Abhik Roychoudhury Roychoudhury

Visiteur de recherche indépendant

Superviseur⋅e principal⋅e :

Postdoctorat - UdeM

Collaborateur·rice alumni - UdeM

Marcin Sendera

Collaborateur·rice alumni - UdeM

Divya Sharma

Postdoctorat

Co-superviseur⋅e :

Alex Hernandez-Garcia

Mélisande Astrid Crystal Teng

Doctorat - UdeM

Co-superviseur⋅e :

Hugo Larochelle

Ivan Titov

Visiteur de recherche indépendant

Superviseur⋅e principal⋅e :

Siva Reddy

Alex Tong

Collaborateur·rice alumni - UdeM

Postdoctorat - 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

Université de Montréal Balancing Signals for Semi-Supervised Sequence Learning

Ya Xu

Chris Pal

Aaron Courville

Training recurrent neural networks (RNNs) on long sequences using backpropagation through time (BPTT) remains a fundamental challenge. It ha… (voir plus)s been shown that adding a local unsupervised loss term into the optimization objective makes the training of RNNs on long sequences more effective. While the importance of an unsupervised task can in principle be controlled by a coefficient in the objective function, the gradients with respect to the unsupervised loss term still influence all the hidden state dimensions, which might cause important information about the supervised task to be degraded or erased. Compared to existing semi-supervised sequence learning methods, this thesis focuses upon a traditionally overlooked mechanism – an architecture with explicitly designed private and shared hidden units designed to mitigate the detrimental influence of the auxiliary unsupervised loss over the main supervised task. We achieve this by dividing the RNN hidden space into a private space for the supervised task or a shared space for both the supervised and unsupervised tasks. We present extensive experiments with the proposed framework on several long sequence modeling benchmark datasets. Results indicate that the proposed framework can yield performance gains in RNN models where long term dependencies are notoriously challenging to deal with.

Untangling tradeoffs between recurrence and self-attention in artificial neural networks

S UPPLEMENTARY M ATERIAL - L EARNING T O N AVIGATE T HE S YNTHETICALLY A CCESSIBLE C HEMICAL S PACE U SING R EINFORCEMENT L EARNING

Sai Krishna

Gottipati

B. Sattarov

Sufeng Niu

Yashaswi Pathak

Haoran Wei

Shengchao Liu

Karam M. J. Thomas

Simon R. Blackburn

Connor Wilson. Coley

Jian Tang

Sarath Chandar

While updating the critic network, we multiply the normal random noise vector with policy noise of 0.2 and then clip it in the range -0.2 to… (voir plus) 0.2. This clipped policy noise is added to the action at the next time step a′ computed by the target actor networks f and π. The actor networks (f and π networks), target critic and target actor networks are updated once every two updates to the critic network.

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

Tong Che

Ruixiang Zhang

Jascha Sohl-Dickstein

Hugo Larochelle

Liam Paull

Yuan Cao

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

Learning from Learning Machines: Optimisation, Rules, and Social Norms

Travis LaCroix

There is an analogy between machine learning systems and economic entities in that they are both adaptive, and their behaviour is specified … (voir plus)in a more-or-less explicit way. It appears that the area of AI that is most analogous to the behaviour of economic entities is that of morally good decision-making, but it is an open question as to how precisely moral behaviour can be achieved in an AI system. This paper explores the analogy between these two complex systems, and we suggest that a clearer understanding of this apparent analogy may help us forward in both the socio-economic domain and the AI domain: known results in economics may help inform feasible solutions in AI safety, but also known results in AI may inform economic policy. If this claim is correct, then the recent successes of deep learning for AI suggest that more implicit specifications work better than explicit ones for solving such problems.

2019-12-29

ArXiv (prépublication)

CLOSURE: Assessing Systematic Generalization of CLEVR Models

Dzmitry Bahdanau

Harm de Vries

Timothy O'Donnell

Shikhar Murty

Philippe Beaudoin

Aaron Courville

2019-12-12

ArXiv (prépublication)

Applying Knowledge Transfer for Water Body Segmentation in Peru

Jessenia Gonzalez

Debjani Bhowmick

César Beltrán

Kris Sankaran

2019-12-02

ArXiv (prépublication)

Artificial Intelligence Based Cloud Distributor (AI-CD): Probing Low Cloud Distribution with Generative Adversarial Neural Networks

T. Yuan

H. Song

David Hall

Victor Schmidt

Kris Sankaran

Automated curriculum generation for Policy Gradients from Demonstrations

Anirudh Srinivasan

Dzmitry Bahdanau

Maxime Chevalier-Boisvert

2019-12-01

ArXiv (prépublication)

Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localization

Md Mahfuzur Rahman Siddiquee

Zongwei Zhou

Nima Tajbakhsh

Ruibin Feng

Michael Gotway

Jianming Liang

Generative adversarial networks (GANs) have ushered in a revolution in image-to-image translation. The development and proliferation of GANs… (voir plus) raises an interesting question: can we train a GAN to remove an object, if present, from an image while otherwise preserving the image? Specifically, can a GAN ``virtually heal'' anyone by turning his medical image, with an unknown health status (diseased or healthy), into a healthy one, so that diseased regions could be revealed by subtracting those two images? Such a task requires a GAN to identify a minimal subset of target pixels for domain translation, an ability that we call fixed-point translation, which no GAN is equipped with yet. Therefore, we propose a new GAN, called Fixed-Point GAN, trained by (1) supervising same-domain translation through a conditional identity loss, and (2) regularizing cross-domain translation through revised adversarial, domain classification, and cycle consistency loss. Based on fixed-point translation, we further derive a novel framework for disease detection and localization using only image-level annotation. Qualitative and quantitative evaluations demonstrate that the proposed method outperforms the state of the art in multi-domain image-to-image translation and that it surpasses predominant weakly-supervised localization methods in both disease detection and localization. Implementation is available at https://github.com/jlianglab/Fixed-Point-GAN.

2019-11-02

2019 IEEE/CVF International Conference on Computer Vision (ICCV) (publié)

doi.org

Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction

Alaaeldin El-Nouby

Shikhar Sharma

Hannes Schulz

(Rex) Devon Hjelm

Layla El Asri

Samira Ebrahimi Kahou

Graham W. Taylor

Conditional text-to-image generation is an active area of research, with many possible applications. Existing research has primarily focused… (voir plus) on generating a single image from available conditioning information in one step. One practical extension beyond one-step generation is a system that generates an image iteratively, conditioned on ongoing linguistic input or feedback. This is significantly more challenging than one-step generation tasks, as such a system must understand the contents of its generated images with respect to the feedback history, the current feedback, as well as the interactions among concepts present in the feedback history. In this work, we present a recurrent image generation model which takes into account both the generated output up to the current step as well as all past instructions for generation. We show that our model is able to generate the background, add new objects, and apply simple transformations to existing objects. We believe our approach is an important step toward interactive generation. Code and data is available at: https://www.microsoft.com/en-us/research/project/generative-neural-visual-artist-geneva/.

2019-11-02

2019 IEEE/CVF International Conference on Computer Vision (ICCV) (publié)

doi.org

Interactive Language Learning by Question Answering

Xingdi Yuan

Adam Trischler

Humans observe and interact with the world to acquire knowledge. However, most existing machine reading comprehension (MRC) tasks miss the i… (voir plus)nteractive, information-seeking component of comprehension. Such tasks present models with static documents that contain all necessary information, usually concentrated in a single short substring. Thus, models can achieve strong performance through simple word- and phrase-based pattern matching. We address this problem by formulating a novel text-based question answering task: Question Answering with Interactive Text (QAit). In QAit, an agent must interact with a partially observable text-based environment to gather information required to answer questions. QAit poses questions about the existence, location, and attributes of objects found in the environment. The data is built using a text-based game generator that defines the underlying dynamics of interaction with the environment. We propose and evaluate a set of baseline models for the QAit task that includes deep reinforcement learning agents. Experiments show that the task presents a major challenge for machine reading systems, while humans solve it with relative ease.

2019-11-01

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (publié)

doi.org