Yoshua Bengio

jamal.abouhaibeh@mila.quebec

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

berkes.anais@mila.quebec

shahana.chatterjee@mila.quebec

Rim Assouel

Doctorat - UdeM

assouelr@mila.quebec

Shahana Chatterjee

Collaborateur·rice de recherche - N/A

sanghyeok.choi@mila.quebec

Xiaoyin Chen

Doctorat - UdeM

xiaoyin.chen@mila.quebec

Sanghyeok Choi

Collaborateur·rice de recherche - KAIST

Doctorat - UdeM

aniket.didolkar@mila.quebec

Visiteur de recherche indépendant

desmond.elliott@mila.quebec

eric.elmoznino@mila.quebec

Eric Elmoznino

Doctorat - UdeM

Doctorat - UdeM

lena-nehale.ezzine@mila.quebec

Jean-Pierre Falet

Doctorat - UdeM

jean-pierre.falet@mila.quebec

Doctorat

pietro.greiner@mila.quebec

Mohsin Hasan

Doctorat - UdeM

mohsin.hasan@mila.quebec

thomas.jiralerspong@mila.quebec

Moksh Jain

Doctorat - UdeM

moksh.jain@mila.quebec

Doctorat - UdeM

Collaborateur·rice alumni - UdeM

younesse.kaddar@mila.quebec

tabitha-edith.lee@mila.quebec

Hyeonah Kim

Postdoctorat - UdeM

hyeonah.kim@mila.quebec

Minsu Kim

Collaborateur·rice de recherche - UdeM

minsu.kim@mila.quebec

Postdoctorat - UdeM

Stagiaire de recherche - KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY

jeongjae.lee@mila.quebec

Collaborateur·rice de recherche

Song LIU

Collaborateur·rice de recherche - s.o.

liusong299@gmail.com

cristian-dragos.manta@mila.quebec

Cristian Dragos Manta

Doctorat - UdeM

Doctorat - UdeM

Visiteur de recherche indépendant - UdeM

nguyenph@mila.quebec

Padideh Nouri

Doctorat - UdeM

padideh.nouri@mila.quebec

Ali Parviz

Collaborateur·rice de recherche - Ying Wu Coll of Computing

ali.parviz@mila.quebec

Lena Podina

Collaborateur·rice de recherche - University of Waterloo

elena.podina@mila.quebec

jarrid.rector-brooks@mila.quebec

Jarrid Rector-Brooks

Doctorat - UdeM

Danyal REHMAN

Postdoctorat - UdeM

danyal.rehman@mila.quebec

oliver.richardson@mila.quebec

Oli RICHARDSON

Postdoctorat - UdeM

Camille Rochefort-Boulanger

Doctorat - UdeM

rochefoc@mila.quebec

Divya Sharma

Postdoctorat

divya.sharma@mila.quebec

Mélisande Astrid Crystal Teng

Collaborateur·rice alumni - UdeM

tengmeli@mila.quebec

alexander.tong@mila.quebec

Alex Tong

Collaborateur·rice alumni - UdeM

Collaborateur·rice de recherche - University of Cambridge

oszkar.urban@mila.quebec

donna.vakalis@mila.quebec

Donna Vakalis

Collaborateur·rice alumni - UdeM

Doctorat - UdeM

siddarth.venkatraman@mila.quebec

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

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

Tianyu Zhang

Doctorat - UdeM

tianyu.zhang@mila.quebec

Doctorat - McGill

Harry Zhao

Collaborateur·rice alumni - McGill

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

CMIM: Cross-Modal Information Maximization For Medical Imaging

Tristan Sylvain

Francis Dutil

Tess Berthier

Lisa Di Jorio

Margaux Luck

R Devon Hjelm

In hospitals, data are siloed to specific information systems that make the same information available under different modalities such as th… (voir plus)e different medical imaging exams the patient undergoes (CT scans, MRI, PET, Ultrasound, etc.) and their associated radiology reports. This offers unique opportunities to obtain and use at train-time those multiple views of the same information that might not always be available at test-time.In this paper, we propose an innovative framework that makes the most of available data by learning good representations of a multi-modal input that are resilient to modality dropping at test-time, using recent advances in mutual information maximization. By maximizing cross-modal information at train time, we are able to outperform several state-of-the-art baselines in two different settings, medical image classification, and segmentation. In particular, our method is shown to have a strong impact on the inference-time performance of weaker modalities.

2021-06-05

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

Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models

Tong Che

Xiaofeng Liu

Site Li

Yubin Ge

Ruixiang Zhang

Caiming Xiong

AI Safety is a major concern in many deep learning applications such as autonomous driving. Given a trained deep learning model, an importan… (voir plus)t natural problem is how to reliably verify the model's prediction. In this paper, we propose a novel framework --- deep verifier networks (DVN) to detect unreliable inputs or predictions of deep discriminative models, using separately trained deep generative models. Our proposed model is based on conditional variational auto-encoders with disentanglement constraints to separate the label information from the latent representation. We give both intuitive and theoretical justifications for the model. Our verifier network is trained independently with the prediction model, which eliminates the need of retraining the verifier network for a new model. We test the verifier network on both out-of-distribution detection and adversarial example detection problems, as well as anomaly detection problems in structured prediction tasks such as image caption generation. We achieve state-of-the-art results in all of these problems.

2021-05-17

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

GraphMix: Improved Training of GNNs for Semi-Supervised Learning

Juho Kannala

We present GraphMix, a regularization method for Graph Neural Network based semi-supervised object classification, whereby we propose to tra… (voir plus)in a fully-connected network jointly with the graph neural network via parameter sharing and interpolation-based regularization. Further, we provide a theoretical analysis of how GraphMix improves the generalization bounds of the underlying graph neural network, without making any assumptions about the "aggregation" layer or the depth of the graph neural networks. We experimentally validate this analysis by applying GraphMix to various architectures such as Graph Convolutional Networks, Graph Attention Networks and Graph-U-Net. Despite its simplicity, we demonstrate that GraphMix can consistently improve or closely match state-of-the-art performance using even simpler architectures such as Graph Convolutional Networks, across three established graph benchmarks: Cora, Citeseer and Pubmed citation network datasets, as well as three newly proposed datasets: Cora-Full, Co-author-CS and Co-author-Physics.

2021-05-17

AAAI Conference on Artificial Intelligence (publié)

Meta-learning framework with applications to zero-shot time-series forecasting

Boris Oreshkin

Dmitri Carpov

Nicolas Chapados

Can meta-learning discover generic ways of processing time series (TS) from a diverse dataset so as to greatly improve generalization on new… (voir plus) TS coming from different datasets? This work provides positive evidence to this using a broad meta-learning framework which we show subsumes many existing meta-learning algorithms. Our theoretical analysis suggests that residual connections act as a meta-learning adaptation mechanism, generating a subset of task-specific parameters based on a given TS input, thus gradually expanding the expressive power of the architecture on-the-fly. The same mechanism is shown via linearization analysis to have the interpretation of a sequential update of the final linear layer. Our empirical results on a wide range of data emphasize the importance of the identified meta-learning mechanisms for successful zero-shot univariate forecasting, suggesting that it is viable to train a neural network on a source TS dataset and deploy it on a different target TS dataset without retraining, resulting in performance that is at least as good as that of state-of-practice univariate forecasting models.

2021-05-17

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

Object-Centric Image Generation from Layouts

Tristan Sylvain

Pengchuan Zhang

R Devon Hjelm

Shikhar Sharma

2021-05-17

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

Parameterizing Branch-and-Bound Search Trees to Learn Branching Policies

Giulia Zarpellon

Jason Jo

Andrea Lodi

Branch and Bound (B&B) is the exact tree search method typically used to solve Mixed-Integer Linear Programming problems (MILPs). Learni… (voir plus)ng branching policies for MILP has become an active research area, with most works proposing to imitate the strong branching rule and specialize it to distinct classes of problems. We aim instead at learning a policy that generalizes across heterogeneous MILPs: our main hypothesis is that parameterizing the state of the B&B search tree can aid this type of generalization. We propose a novel imitation learning framework, and introduce new input features and architectures to represent branching. Experiments on MILP benchmark instances clearly show the advantages of incorporating an explicit parameterization of the state of the search tree to modulate the branching decisions, in terms of both higher accuracy and smaller B&B trees. The resulting policies significantly outperform the current state-of-the-art method for "learning to branch" by effectively allowing generalization to generic unseen instances.

2021-05-17

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

Visual Concept Reasoning Networks

Taesup Kim

Sungwoong Kim

2021-05-17

AAAI Conference on Artificial Intelligence (publié)

How does hemispheric specialization contribute to human-defining cognition?

Gesa Hartwigsen

Danilo Bzdok

2021-05-10

Neuron (publié)

Predicting Infectiousness for Proactive Contact Tracing

Prateek Gupta

Nasim Rahaman

Pierre-Luc St-Charles

Hannah Alsdurf

Olexa Bilanuik

David Buckeridge

Gaétan Marceau Caron

Pierre-Luc Carrier

Joumana Ghosn

Satya Ortiz-Gagné

Chris Pal

Irina Rish

Bernhard Schölkopf … (voir 3 de plus)

Abhinav Sharma

Jian Tang

Andrew Williams

The COVID-19 pandemic has spread rapidly worldwide, overwhelming manual contact tracing in many countries and resulting in widespread lockdo… (voir plus)wns for emergency containment. Large-scale digital contact tracing (DCT) has emerged as a potential solution to resume economic and social activity while minimizing spread of the virus. Various DCT methods have been proposed, each making trade-offs between privacy, mobility restrictions, and public health. The most common approach, binary contact tracing (BCT), models infection as a binary event, informed only by an individual's test results, with corresponding binary recommendations that either all or none of the individual's contacts quarantine. BCT ignores the inherent uncertainty in contacts and the infection process, which could be used to tailor messaging to high-risk individuals, and prompt proactive testing or earlier warnings. It also does not make use of observations such as symptoms or pre-existing medical conditions, which could be used to make more accurate infectiousness predictions. In this paper, we use a recently-proposed COVID-19 epidemiological simulator to develop and test methods that can be deployed to a smartphone to locally and proactively predict an individual's infectiousness (risk of infecting others) based on their contact history and other information, while respecting strong privacy constraints. Predictions are used to provide personalized recommendations to the individual via an app, as well as to send anonymized messages to the individual's contacts, who use this information to better predict their own infectiousness, an approach we call proactive contact tracing (PCT). We find a deep-learning based PCT method which improves over BCT for equivalent average mobility, suggesting PCT could help in safe re-opening and second-wave prevention.

2021-05-02

International Conference on Learning Representations (Spotlight)

Saliency Is a Possible Red Herring When Diagnosing Poor Generalization

Joseph D. Viviano

Becks Simpson

Francis Dutil

Joseph Paul Cohen

Poor generalization is one symptom of models that learn to predict target variables using spuriously-correlated image features present only … (voir plus)in the training distribution instead of the true image features that denote a class. It is often thought that this can be diagnosed visually using attribution (aka saliency) maps. We study if this assumption is correct. In some prediction tasks, such as for medical images, one may have some images with masks drawn by a human expert, indicating a region of the image containing relevant information to make the prediction. We study multiple methods that take advantage of such auxiliary labels, by training networks to ignore distracting features which may be found outside of the region of interest. This mask information is only used during training and has an impact on generalization accuracy depending on the severity of the shift between the training and test distributions. Surprisingly, while these methods improve generalization performance in the presence of a covariate shift, there is no strong correspondence between the correction of attribution towards the features a human expert has labelled as important and generalization performance. These results suggest that the root cause of poor generalization may not always be spatially defined, and raise questions about the utility of masks as "attribution priors" as well as saliency maps for explainable predictions.

2021-05-02

International Conference on Learning Representations (Poster)

hBERT + BiasCorp - Fighting Racism on the Web

Olawale Moses Onabola

Zhuang Ma

Xie Yang

Benjamin Akera

Ibraheem Abdulrahman

Jia Xue

Dianbo Liu

Subtle and overt racism is still present both in physical and online communities today and has impacted many lives in different segments of … (voir plus)the society. In this short piece of work, we present how we’re tackling this societal issue with Natural Language Processing. We are releasing BiasCorp, a dataset containing 139,090 comments and news segment from three specific sources - Fox News, BreitbartNews and YouTube. The first batch (45,000 manually annotated) is ready for publication. We are currently in the final phase of manually labeling the remaining dataset using Amazon Mechanical Turk. BERT has been used widely in several downstream tasks. In this work, we present hBERT, where we modify certain layers of the pretrained BERT model with the new Hopfield Layer. hBert generalizes well across different distributions with the added advantage of a reduced model complexity. We are also releasing a JavaScript library 3 and a Chrome Extension Application, to help developers make use of our trained model in web applications (say chat application) and for users to identify and report racially biased contents on the web respectively

2021-04-18

OpenReview.net/Archive (publié)

Neural Function Modules with Sparse Arguments: A Dynamic Approach to Integrating Information across Layers

Alex Lamb

Anirudh Goyal

Agnieszka Słowik

Michael Mozer

Philippe Beaudoin

Feed-forward neural networks consist of a sequence of layers, in which each layer performs some processing on the information from the previ… (voir plus)ous layer. A downside to this approach is that each layer (or module, as multiple modules can operate in parallel) is tasked with processing the entire hidden state, rather than a particular part of the state which is most relevant for that module. Methods which only operate on a small number of input variables are an essential part of most programming languages, and they allow for improved modularity and code re-usability. Our proposed method, Neural Function Modules (NFM), aims to introduce the same structural capability into deep learning. Most of the work in the context of feed-forward networks combining top-down and bottom-up feedback is limited to classification problems. The key contribution of our work is to combine attention, sparsity, top-down and bottom-up feedback, in a flexible algorithm which, as we show, improves the results in standard classification, out-of-domain generalization, generative modeling, and learning representations in the context of reinforcement learning.

2021-03-17

Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (publié)