Guillaume Lajoie

Biography

Guillaume Lajoie is an Associate professor in the Department of Mathematics and Statistics at Université de Montréal and a Core Academic Member of Mila – Quebec Artificial Intelligence Institute. He holds a Canada-CIFAR AI Research Chair, and a Canada Research Chair (CRC) in Neural Computation and Interfacing.

His research is positioned at the intersection of AI and Neuroscience where he develops tools to better understand mechanisms of intelligence common to both biological and artificial systems. His research group's contributions range from advances in multi-scale learning paradigms for large artificial systems, to applications in neurotechnology. Dr. Lajoie is actively involved in responsible AI development efforts, seeking to identify guidelines and best practices for use of AI in research and beyond.

Current Students

Federico Arangath Joseph

Collaborating researcher - ETH Zurich

Stefan Bauer

Independent visiting researcher

Principal supervisor :

Sangnie Bhardwaj

PhD - Université de Montréal

Co-supervisor :

Hugo Larochelle

Colin Bredenberg

Postdoctorate - Université de Montréal

Co-supervisor :

Blake Richards

Leo Choiniere

PhD - Université de Montréal

Olivier Codol

Postdoctorate - Université de Montréal

Co-supervisor :

PhD - Université de Montréal

Principal supervisor :

PhD - Université de Montréal

Principal supervisor :

Leo Gagnon

PhD - Université de Montréal

Juan Guerra

Master's Research - Polytechnique Montréal

Principal supervisor :

Marco Bonizzato

Website

Nanda Harishankar Krishna

PhD - Université de Montréal

Collaborating researcher - Western Washington University (faculty; assistant prof))

Principal supervisor :

Master's Research - Université de Montréal

Co-supervisor :

Collaborating researcher - Université de Montréal

Ximeng Mao

PhD - Université de Montréal

Co-supervisor :

Joelle Pineau

Abdel Mfougouon Njupoun

PhD - Université de Montréal

Co-supervisor :

PhD - Université de Montréal

Co-supervisor :

Amine Natik

PhD - Université de Montréal

Co-supervisor :

Guy Wolf

François Paugam

PhD - Université de Montréal

Principal supervisor :

Lune Bellec

Alexandre Payeur

Collaborating researcher - Université de Montréal

Mohammad Pezeshki

Collaborating researcher

Principal supervisor :

Postdoctorate - McGill University

Principal supervisor :

Param Raval

Collaborating Alumni - Université de Montréal

Avery Ryoo

Master's Research - Université de Montréal

Principal supervisor :

PhD - Université de Montréal

Co-supervisor :

Lune Bellec

Laura Suarez

PhD - McGill University

Ryan Vogt

Postdoctorate - Université de Montréal

Vivian White

Research Intern - Western Washington University

Co-supervisor :

PhD - Université de Montréal

Website

What Do Synaptic Weight Distributions Tell Us About Learning in the Brain ?

Blog Posts

June 13, 2024

Roman Pogodin

Jonathan Cornford

Arna Ghosh

Gauthier Gidel

Guillaume Lajoie

Blake Richards

Read the article

Publications

Implicit Regularization in Deep Learning: A View from Function Space

Aristide Baratin

Thomas George

César Laurent

2020-08-03

ArXiv (preprint)

arxiv.org

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… (see more)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 (preprint)

arxiv.org

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 (published)

proceedings.mlr.press

arxiv.org

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… (see more)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