Yoshua Bengio

jamal.abouhaibeh@mila.quebec

Biographie

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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

Collaborateur·rice de recherche - Cambridge University

Superviseur⋅e principal⋅e :

David Rolnick

berkes.anais@mila.quebec

sanghyeok.choi@mila.quebec

Rim Assouel

Doctorat - UdeM

assouelr@mila.quebec

Xiaoyin Chen

Doctorat - UdeM

xiaoyin.chen@mila.quebec

Sanghyeok Choi

Collaborateur·rice de recherche - KAIST

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

Mohsin Hasan

Doctorat - UdeM

mohsin.hasan@mila.quebec

thomas.jiralerspong@mila.quebec

Moksh Jain

Doctorat - UdeM

moksh.jain@mila.quebec

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Guillaume Lajoie

Collaborateur·rice alumni - UdeM

younesse.kaddar@mila.quebec

Minsu Kim

Collaborateur·rice de recherche - UdeM

minsu.kim@mila.quebec

Hyeonah Kim

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Alex Hernández-García

hyeonah.kim@mila.quebec

Jeongjae Lee

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

jeongjae.lee@mila.quebec

cristian-dragos.manta@mila.quebec

Song LIU

Collaborateur·rice de recherche - s.o.

Collaborateur·rice de recherche

Cristian Dragos Manta

Doctorat - UdeM

Co-superviseur⋅e :

Dhanya Sridhar

Sarthak Mittal

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Visiteur de recherche indépendant - UdeM

nguyenph@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

Superviseur⋅e principal⋅e :

David Rolnick

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

Superviseur⋅e principal⋅e :

Postdoctorat - UdeM

Co-superviseur⋅e :

Alex Hernández-García

divya.sharma@mila.quebec

Mélisande Astrid Crystal Teng

Collaborateur·rice alumni - UdeM

Co-superviseur⋅e :

Alex Tong

Collaborateur·rice alumni - UdeM

alexander.tong@mila.quebec

Collaborateur·rice de recherche - University of Cambridge

oszkar.urban@mila.quebec

donna.vakalis@mila.quebec

Donna Vakalis

Collaborateur·rice alumni - UdeM

Co-superviseur⋅e :

David Rolnick

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Glen Berseth

siddarth.venkatraman@mila.quebec

Collaborateur·rice de recherche

joseph.viviano2@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

Superviseur⋅e principal⋅e :

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

On Adversarial Mixup Resynthesis

R Devon Hjelm

Christopher Pal

In this paper, we explore new approaches to combining information encoded within the learned representations of auto-encoders. We explore mo… (voir plus)dels that are capable of combining the attributes of multiple inputs such that a resynthesised output is trained to fool an adversarial discriminator for real versus synthesised data. Furthermore, we explore the use of such an architecture in the context of semi-supervised learning, where we learn a mixing function whose objective is to produce interpolations of hidden states, or masked combinations of latent representations that are consistent with a conditioned class label. We show quantitative and qualitative evidence that such a formulation is an interesting avenue of research.

2019-03-06

Neural Information Processing Systems (publié)

dblp.uni-trier.de

Learning Dynamics Model in Reinforcement Learning by Incorporating the Long Term Future

Nan Rosemary Ke

Amanpreet Singh

Ahmed Touati

Anirudh Goyal

Devi Parikh

Dhruv Batra

In model-based reinforcement learning, the agent interleaves between model learning and planning. These two components are inextricably inte… (voir plus)rtwined. If the model is not able to provide sensible long-term prediction, the executed planner would exploit model flaws, which can yield catastrophic failures. This paper focuses on building a model that reasons about the long-term future and demonstrates how to use this for efficient planning and exploration. To this end, we build a latent-variable autoregressive model by leveraging recent ideas in variational inference. We argue that forcing latent variables to carry future information through an auxiliary task substantially improves long-term predictions. Moreover, by planning in the latent space, the planner's solution is ensured to be within regions where the model is valid. An exploration strategy can be devised by searching for unlikely trajectories under the model. Our method achieves higher reward faster compared to baselines on a variety of tasks and environments in both the imitation learning and model-based reinforcement learning settings.

2019-03-04

arXiv (prépublication)

arxiv.org

Hyperbolic Discounting and Learning over Multiple Horizons

William Fedus

Carles Gelada

Bellemare Marc-Emmanuel

Hugo Larochelle

Reinforcement learning (RL) typically defines a discount factor as part of the Markov Decision Process. The discount factor values future re… (voir plus)wards by an exponential scheme that leads to theoretical convergence guarantees of the Bellman equation. However, evidence from psychology, economics and neuroscience suggests that humans and animals instead have hyperbolic time-preferences. In this work we revisit the fundamentals of discounting in RL and bridge this disconnect by implementing an RL agent that acts via hyperbolic discounting. We demonstrate that a simple approach approximates hyperbolic discount functions while still using familiar temporal-difference learning techniques in RL. Additionally, and independent of hyperbolic discounting, we make a surprising discovery that simultaneously learning value functions over multiple time-horizons is an effective auxiliary task which often improves over a strong value-based RL agent, Rainbow.

2019-02-18

ArXiv (prépublication)

Equivalence of Equilibrium Propagation and Recurrent Backpropagation

Benjamin Scellier

Recurrent backpropagation and equilibrium propagation are supervised learning algorithms for fixed-point recurrent neural networks, which di… (voir plus)ffer in their second phase. In the first phase, both algorithms converge to a fixed point that corresponds to the configuration where the prediction is made. In the second phase, equilibrium propagation relaxes to another nearby fixed point corresponding to smaller prediction error, whereas recurrent backpropagation uses a side network to compute error derivatives iteratively. In this work, we establish a close connection between these two algorithms. We show that at every moment in the second phase, the temporal derivatives of the neural activities in equilibrium propagation are equal to the error derivatives computed iteratively by recurrent backpropagation in the side network. This work shows that it is not required to have a side network for the computation of error derivatives and supports the hypothesis that in biological neural networks, temporal derivatives of neural activities may code for error signals.

2019-01-31

Neural Computation (publié)

Maximum Entropy Generators for Energy-Based Models

Maximum likelihood estimation of energy-based models is a challenging problem due to the intractability of the log-likelihood gradient. In t… (voir plus)his work, we propose learning both the energy function and an amortized approximate sampling mechanism using a neural generator network, which provides an efficient approximation of the log-likelihood gradient. The resulting objective requires maximizing entropy of the generated samples, which we perform using recently proposed nonparametric mutual information estimators. Finally, to stabilize the resulting adversarial game, we use a zero-centered gradient penalty derived as a necessary condition from the score matching literature. The proposed technique can generate sharp images with Inception and FID scores competitive with recent GAN techniques, does not suffer from mode collapse, and is competitive with state-of-the-art anomaly detection techniques.

2019-01-23

ArXiv (prépublication)

arxiv.org

The Benefits of Over-parameterization at Initialization in Deep ReLU Networks

Devansh Arpit

It has been noted in existing literature that over-parameterization in ReLU networks generally improves performance. While there could be se… (voir plus)veral factors involved behind this, we prove some desirable theoretical properties at initialization which may be enjoyed by ReLU networks. Specifically, it is known that He initialization in deep ReLU networks asymptotically preserves variance of activations in the forward pass and variance of gradients in the backward pass for infinitely wide networks, thus preserving the flow of information in both directions. Our paper goes beyond these results and shows novel properties that hold under He initialization: i) the norm of hidden activation of each layer is equal to the norm of the input, and, ii) the norm of weight gradient of each layer is equal to the product of norm of the input vector and the error at output layer. These results are derived using the PAC analysis framework, and hold true for finitely sized datasets such that the width of the ReLU network only needs to be larger than a certain finite lower bound. As we show, this lower bound depends on the depth of the network and the number of samples, and by the virtue of being a lower bound, over-parameterized ReLU networks are endowed with these desirable properties. For the aforementioned hidden activation norm property under He initialization, we further extend our theory and show that this property holds for a finite width network even when the number of data samples is infinite. Thus we overcome several limitations of existing papers, and show new properties of deep ReLU networks at initialization.

2019-01-10

ArXiv (prépublication)

Adversarial Domain Adaptation for Stable Brain-Machine Interfaces

Ali Farshchian

Juan A. Gallego

Joseph Paul Cohen

Lee Miller

Sara Solla

Brain-Machine Interfaces (BMIs) have recently emerged as a clinically viable option to restore voluntary movements after paralysis. These de… (voir plus)vices are based on the ability to extract information about movement intent from neural signals recorded using multi-electrode arrays chronically implanted in the motor cortices of the brain. However, the inherent loss and turnover of recorded neurons requires repeated recalibrations of the interface, which can potentially alter the day-to-day user experience. The resulting need for continued user adaptation interferes with the natural, subconscious use of the BMI. Here, we introduce a new computational approach that decodes movement intent from a low-dimensional latent representation of the neural data. We implement various domain adaptation methods to stabilize the interface over significantly long times. This includes Canonical Correlation Analysis used to align the latent variables across days; this method requires prior point-to-point correspondence of the time series across domains. Alternatively, we match the empirical probability distributions of the latent variables across days through the minimization of their Kullback-Leibler divergence. These two methods provide a significant and comparable improvement in the performance of the interface. However, implementation of an Adversarial Domain Adaptation Network trained to match the empirical probability distribution of the residuals of the reconstructed neural signals outperforms the two methods based on latent variables, while requiring remarkably few data points to solve the domain adaptation problem.

2018-12-31

ICLR.cc/2019/Conference (poster)

Artificial Intelligence Cytometer in Blood

Geoffrey Hinton

2018-12-31

(publié)

www.semanticscholar.org

BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning

Maxime Chevalier-Boisvert

Allowing humans to interactively train artificial agents to understand language instructions is desirable for both practical and scientific … (voir plus)reasons, but given the poor data efficiency of the current learning methods, this goal may require substantial research efforts. Here, we introduce the BabyAI research platform to support investigations towards including humans in the loop for grounded language learning. The BabyAI platform comprises an extensible suite of 19 levels of increasing difficulty. The levels gradually lead the agent towards acquiring a combinatorially rich synthetic language which is a proper subset of English. The platform also provides a heuristic expert agent for the purpose of simulating a human teacher. We report baseline results and estimate the amount of human involvement that would be required to train a neural network-based agent on some of the BabyAI levels. We put forward strong evidence that current deep learning methods are not yet sufficiently sample efficient when it comes to learning a language with compositional properties.

2018-12-31

International Conference on Learning Representations (poster)

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.

2018-12-31

ICLR (Poster) (publié)

An Empirical Study of Example Forgetting During Deep Neural Network Learning

Mariya Toneva

Alessandro Sordoni

Remi Tachet des Combes

Adam Trischler

Geoffrey J. Gordon

Inspired by the phenomenon of catastrophic forgetting, we investigate the learning dynamics of neural networks as they train on single class… (voir plus)ification tasks. Our goal is to understand whether a related phenomenon occurs when data does not undergo a clear distributional shift. We define a `forgetting event' to have occurred when an individual training example transitions from being classified correctly to incorrectly over the course of learning. Across several benchmark data sets, we find that: (i) certain examples are forgotten with high frequency, and some not at all; (ii) a data set's (un)forgettable examples generalize across neural architectures; and (iii) based on forgetting dynamics, a significant fraction of examples can be omitted from the training data set while still maintaining state-of-the-art generalization performance.

2018-12-31

ICLR.cc/2019/Conference (poster)

Gradient based sample selection for online continual learning

A continual learning agent learns online with a non-stationary and never-ending stream of data. The key to such learning process is to overc… (voir plus)ome the catastrophic forgetting of previously seen data, which is a well known problem of neural networks. To prevent forgetting, a replay buffer is usually employed to store the previous data for the purpose of rehearsal. Previous works often depend on task boundary and i.i.d. assumptions to properly select samples for the replay buffer. In this work, we formulate sample selection as a constraint reduction problem based on the constrained optimization view of continual learning. The goal is to select a fixed subset of constraints that best approximate the feasible region defined by the original constraints. We show that it is equivalent to maximizing the diversity of samples in the replay buffer with parameters gradient as the feature. We further develop a greedy alternative that is cheap and efficient. The advantage of the proposed method is demonstrated by comparing to other alternatives under the continual learning setting. Further comparisons are made against state of the art methods that rely on task boundaries which show comparable or even better results for our method.

2018-12-31

Neural Information Processing Systems (publié)