Guillaume Lajoie

Biographie

Guillaume Lajoie est professeur agrégé au Département de mathématiques et de statistiques (DMS) de l'Université de Montréal et membre académique principal de Mila – Institut québécois d’intelligence artificielle. Il est titulaire d'une chaire CIFAR (CCAI Canada) ainsi que d'une chaire de recherche du Canada (CRC) en calcul et interfaçage neuronaux.

Ses recherches sont positionnées à l'intersection de l'IA et des neurosciences où il développe des outils pour mieux comprendre les mécanismes d'intelligence communs aux systèmes biologiques et artificiels. Les contributions de son groupe de recherche vont des progrès des paradigmes d'apprentissage à plusieurs échelles pour les grands systèmes artificiels aux applications en neurotechnologie. Dr. Lajoie participe activement aux efforts de développement responsables de l'IA, cherchant à identifier les lignes directrices et les meilleures pratiques pour l'utilisation de l'IA dans la recherche et au-delà.

Étudiants actuels

Federico Arangath Joseph

Collaborateur·rice de recherche - ETH Zurich

Stefan Bauer

Visiteur de recherche indépendant

Superviseur⋅e principal⋅e :

Sangnie Bhardwaj

Doctorat - UdeM

Co-superviseur⋅e :

Hugo Larochelle

Colin Bredenberg

Postdoctorat - UdeM

Co-superviseur⋅e :

Blake Richards

Leo Choiniere

Doctorat - UdeM

Olivier Codol

Postdoctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Leo Gagnon

Doctorat - UdeM

Skylar Gu

Stagiaire de recherche - McGill

Superviseur⋅e principal⋅e :

Dhanya Sridhar

Juan Guerra

Maîtrise recherche - Polytechnique

Superviseur⋅e principal⋅e :

Marco Bonizzato

Site web

Nanda Harishankar Krishna

Doctorat - UdeM

Collaborateur·rice de recherche - Western Washington University (faculty; assistant prof))

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Co-superviseur⋅e :

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Dhanya Sridhar

tejaskasetty@gmail.com

Site web

Ximeng Mao

Doctorat - UdeM

Co-superviseur⋅e :

Joelle Pineau

Abdel Mfougouon Njupoun

Doctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Co-superviseur⋅e :

Amine Natik

Doctorat - UdeM

Co-superviseur⋅e :

Guy Wolf

Alexandre Payeur

Collaborateur·rice de recherche - UdeM

Mohammad Pezeshki

Collaborateur·rice de recherche

Superviseur⋅e principal⋅e :

Collaborateur·rice alumni - McGill

Superviseur⋅e principal⋅e :

Julia Price

Maîtrise recherche - UdeM

Param Raval

Collaborateur·rice alumni - UdeM

Avery Ryoo

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Co-superviseur⋅e :

Lune Bellec

Ayesha Vermani

Visiteur de recherche indépendant - Champalimeau Institute for the Unknown

Ryan Vogt

Postdoctorat - UdeM

Apprentissage automatique pour la segmentation des différentes activations des fibres nerveuses à partir des signaux neuronaux du cerveau vers le corps

Vivian White

Stagiaire de recherche - Western Washington University

Co-superviseur⋅e :

Doctorat - UdeM

Billets de blogue

Représentation graphique d'un nerf vague

21 mai 2025

par

Param Raval

Olivier Tessier-Larivière

Pascal Fortier-Poisson

Blake Richards

Guillaume Lajoie

Lire l'article

13 juin 2024

Que nous apprennent les distributions des coefficients synaptiques au sujet de l’apprentissage dans le cerveau ?

par

Roman Pogodin

Jonathan Cornford

Arna Ghosh

Gauthier Gidel

Guillaume Lajoie

Blake Richards

Lire l'article

Publications

Embedding Signals on Knowledge Graphs with Unbalanced Diffusion Earth Mover's Distance

Alexander Tong

Guillaume Huguet

Dennis L. Shung

Amine Natik

Manik Kuchroo

Guy Wolf

Smita Krishnaswamy

In modern relational machine learning it is common to encounter large graphs that arise via interactions or similarities between observation… (voir plus)s in many domains. Further

2021-01-01

arXiv.org (preprint)

dblp.uni-trier.de

Implicit Regularization in Deep Learning: A View from Function Space

Aristide Baratin

Thomas George

César Laurent

We approach the problem of implicit regularization in deep learning from a geometrical viewpoint. We highlight a possible regularization eff… (voir plus)ect induced by a dynamical alignment of the neural tangent features introduced by Jacot et al, along a small number of task-relevant directions. By extrapolating a new analysis of Rademacher complexity bounds in linear models, we propose and study a new heuristic complexity measure for neural networks which captures this phenomenon, in terms of sequences of tangent kernel classes along in the learning trajectories.

2020-08-03

ArXiv (preprint)

Implicit Regularization in Deep Learning: A View from Function Space

Aristide Baratin

Thomas George

César Laurent

2020-08-03

ArXiv (prépublication)

Untangling tradeoffs between recurrence and self-attention in neural networks

Giancarlo Kerg

Bhargav Kanuparthi

Anirudh Goyal

Kyle Goyette

Attention and self-attention mechanisms, inspired by cognitive processes, are now central to state-of-the-art deep learning on sequential ta… (voir plus)sks. However, most recent progress hinges on heuristic approaches with limited understanding of attention's role in model optimization and computation, and rely on considerable memory and computational resources that scale poorly. In this work, we present a formal analysis of how self-attention affects gradient propagation in recurrent networks, and prove that it mitigates the problem of vanishing gradients when trying to capture long-term dependencies. Building on these results, we propose a relevancy screening mechanism, inspired by the cognitive process of memory consolidation, that allows for a scalable use of sparse self-attention with recurrence. While providing guarantees to avoid vanishing gradients, we use simple numerical experiments to demonstrate the tradeoffs in performance and computational resources by efficiently balancing attention and recurrence. Based on our results, we propose a concrete direction of research to improve scalability of attentive networks.

2020-06-16

ArXiv (prépublication)

Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules

Sarthak Mittal

Alex Lamb

Anirudh Goyal

Vikram Voleti

Murray P. Shanahan

Michael Curtis Mozer

2020-01-01

ICML (publié)

proceedings.mlr.press

Learning Long-term Dependencies Using Cognitive Inductive Biases in Self-attention RNNs

Giancarlo Kerg

Bhargav Kanuparthi

Anirudh Goyal

Kyle Goyette

Attention and self-attention mechanisms, inspired by cognitive processes, are now central to state-of-the-art deep learning on sequential ta… (voir plus)sks. However, most recent progress hinges on heuristic approaches that rely on considerable memory and computational resources that scale poorly. In this work, we propose a relevancy screening mechanism, inspired by the cognitive process of memory consolidation, that allows for a scalable use of sparse self-attention with recurrence. We use simple numerical experiments to demonstrate that this mechanism helps enable recurrent systems on generalization and transfer learning tasks. Based on our results, we propose a concrete direction of research to improve scalability and generalization of attentive recurrent networks.

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

Giancarlo Kerg

Bhargav Kanuparthi

Anirudh Goyal

Kyle Goyette