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 :

Matt Perich

Eric Elmoznino

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Leo Gagnon

Doctorat - UdeM

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 :

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Collaborateur·rice de recherche - UdeM

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

François Paugam

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Lune Bellec

Alexandre Payeur

Collaborateur·rice de recherche - UdeM

Mohammad Pezeshki

Collaborateur·rice de recherche

Superviseur⋅e principal⋅e :

Postdoctorat - McGill

Superviseur⋅e principal⋅e :

Param Raval

Collaborateur·rice alumni - UdeM

Avery Ryoo

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Co-superviseur⋅e :

Lune Bellec

Laura Suarez

Doctorat - McGill

Ryan Vogt

Postdoctorat - UdeM

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

Vivian White

Stagiaire de recherche - Western Washington University

Co-superviseur⋅e :

Doctorat - UdeM

Billets de blogue

13 juin 2024

par

Roman Pogodin

Jonathan Cornford

Arna Ghosh

Gauthier Gidel

Guillaume Lajoie

Blake Richards

Lire l'article

Publications

Online Bayesian optimization of vagus nerve stimulation.

Lorenz Wernisch

Tristan Edwards

Antonin Berthon

Olivier Tessier-Lariviere

Elvijs Sarkans

Myrta Stoukidi

Pascal Fortier-Poisson

Max Pinkney

Michael Thornton

Catherine Hanley

Susannah Lee

Joel Jennings

Ben Appleton

Philip Garsed

Bret Patterson

Buttinger Will

Samuel Gonshaw

Matjaž Jakopec

Sudhakaran Shunmugam

Jorin Mamen … (voir 4 de plus)

Aleksi Tukiainen

Oliver Armitage

Emil Hewage

OBJECTIVE In bioelectronic medicine, neuromodulation therapies induce neural signals to the brain or organs, modifying their function. Stimu… (voir plus)lation devices capable of triggering exogenous neural signals using electrical waveforms require a complex and multi-dimensional parameter space to control such waveforms. Determining the best combination of parameters (waveform optimization or dosing) for treating a particular patient's illness is therefore challenging. Comprehensive parameter searching for an optimal stimulation effect is often infeasible in a clinical setting due to the size of the parameter space. Restricting this space, however, may lead to suboptimal therapeutic results, reduced responder rates, and adverse effects. Approach. As an alternative to a full parameter search, we present a flexible machine learning, data acquisition, and processing framework for optimizing neural stimulation parameters, requiring as few steps as possible using Bayesian optimization. This optimization builds a model of the neural and physiological responses to stimulations, enabling it to optimize stimulation parameters and provide estimates of the accuracy of the response model. The vagus nerve innervates, among other thoracic and visceral organs, the heart, thus controlling heart rate, making it an ideal candidate for demonstrating the effectiveness of our approach. Main results. The efficacy of our optimization approach was first evaluated on simulated neural responses, then applied to vagus nerve stimulation intraoperatively in porcine subjects. Optimization converged quickly on parameters achieving target heart rates and optimizing neural B-fiber activations despite high intersubject variability. Significance. An optimized stimulation waveform was achieved in real time with far fewer stimulations than required by alternative optimization strategies, thus minimizing exposure to side effects. Uncertainty estimates helped avoiding stimulations outside a safe range. Our approach shows that a complex set of neural stimulation parameters can be optimized in real-time for a patient to achieve a personalized precision dosing. .

2024-03-13

Journal of Neural Engineering (publié)

Explicit Knowledge Factorization Meets In-Context Learning: What Do We Gain?

Sarthak Mittal

Eric Elmoznino

Leo Gagnon

Sangnie Bhardwaj

Dhanya Sridhar

2024-03-05

ICLR.cc/2024/Workshop/R2-FM (poster)

Learning and Aligning Structured Random Feature Networks

Vivian White

Muawiz Sajjad Chaudhary

Guy Wolf

Kameron Decker Harris

Artificial neural networks (ANNs) are considered "black boxes'' due to the difficulty of interpreting their learned weights. While choosing… (voir plus) the best features is not well understood, random feature networks (RFNs) and wavelet scattering ground some ANN learning mechanisms in function space with tractable mathematics. Meanwhile, the genetic code has evolved over millions of years, shaping the brain to develop variable neural circuits with reliable structure that resemble RFNs. We explore a similar approach, embedding neuro-inspired, wavelet-like weights into multilayer RFNs. These can outperform scattering and have kernels that describe their function space at large width. We build learnable and deeper versions of these models where we can optimize separate spatial and channel covariances of the convolutional weight distributions. We find that these networks can perform comparatively with conventional ANNs while dramatically reducing the number of trainable parameters. Channel covariances are most influential, and both weight and activation alignment are needed for classification performance. Our work outlines how neuro-inspired configurations may lead to better performance in key cases and offers a potentially tractable reduced model for ANN learning.

2024-03-02

ICLR.cc/2024/Workshop/Re-Align (poster)

Learning and Aligning Structured Random Feature Networks

Vivian White

Muawiz Sajjad Chaudhary

Guy Wolf

Kameron Decker Harris

Artificial neural networks (ANNs) are considered ``black boxes'' due to the difficulty of interpreting their learned weights. While choosin… (voir plus)g the best features is not well understood, random feature networks (RFNs) and wavelet scattering ground some ANN learning mechanisms in function space with tractable mathematics. Meanwhile, the genetic code has evolved over millions of years, shaping the brain to devlop variable neural circuits with reliable structure that resemble RFNs. We explore a similar approach, embedding neuro-inspired, wavelet-like weights into multilayer RFNs. These can outperform scattering and have kernels that describe their function space at large width. We build learnable and deeper versions of these models where we can optimize separate spatial and channel covariances of the convolutional weight distributions. We find that these networks can perform comparatively with conventional ANNs while dramatically reducing the number of trainable parameters. Channel covariances are most influential, and both weight and activation alignment are needed for classification performance. Our work outlines how neuro-inspired configurations may lead to better performance in key cases and offers a potentially tractable reduced model for ANN learning.

2024-03-02

ICLR.cc/2024/Workshop/Re-Align (poster)

Sources of richness and ineffability for phenomenally conscious states

Xu Ji

Eric Elmoznino

George Deane

Axel Constant

Guillaume Dumas

Jonathan Simon

2024-03-01

Neuroscience of Consciousness (publié)

arxiv.org

Gaussian-process-based Bayesian optimization for neurostimulation interventions in rats

Léo Choinière

Rose Guay-Hottin

Rémi Picard

Marco Bonizzato

Numa Dancause

2024-02-14

STAR Protocols (publié)

Connectome-based reservoir computing with the conn2res toolbox

Laura E. Suárez

Agoston Mihalik

Filip Milisav

Kenji Marshall

Mingze Li

Petra E. Vértes

Bratislav Mišić

2024-01-22

Nature Communications (publié)

Amortizing intractable inference in large language models

Edward J Hu

Moksh J. Jain

Eric Elmoznino

Younesse Kaddar

Nikolay Malkin

Autoregressive large language models (LLMs) compress knowledge from their training data through next-token conditional distributions. This l… (voir plus)imits tractable querying of this knowledge to start-to-end autoregressive sampling. However, many tasks of interest -- including sequence continuation, infilling, and other forms of constrained generation -- involve sampling from intractable posterior distributions. We address this limitation by using amortized Bayesian inference to sample from these intractable posteriors. Such amortization is algorithmically achieved by fine-tuning LLMs via diversity-seeking reinforcement learning algorithms: generative flow networks (GFlowNets). We empirically demonstrate that this distribution-matching paradigm of LLM fine-tuning can serve as an effective alternative to maximum-likelihood training and reward-maximizing policy optimization. As an important application, we interpret chain-of-thought reasoning as a latent variable modeling problem and demonstrate that our approach enables data-efficient adaptation of LLMs to tasks that require multi-step rationalization and tool use.

2024-01-16

ICLR.cc/2024/Conference (présentation orale)

Delta-AI: Local objectives for amortized inference in sparse graphical models

Jean-Pierre R. Falet

Hae Beom Lee

Nikolay Malkin

Chen Sun

Dragos Secrieru

Dinghuai Zhang

We present a new algorithm for amortized inference in sparse probabilistic graphical models (PGMs), which we call …

2024-01-16

ICLR.cc/2024/Conference (poster)

How connectivity structure shapes rich and lazy learning in neural circuits

Yuhan Helena Liu

Aristide Baratin

Jonathan Cornford

Stefan Mihalas

Eric Todd SheaBrown

In theoretical neuroscience, recent work leverages deep learning tools to explore how some network attributes critically influence its learn… (voir plus)ing dynamics. Notably, initial weight distributions with small (resp. large) variance may yield a rich (resp. lazy) regime, where significant (resp. minor) changes to network states and representation are observed over the course of learning. However, in biology, neural circuit connectivity generally has a low-rank structure and therefore differs markedly from the random initializations generally used for these studies. As such, here we investigate how the structure of the initial weights — in particular their effective rank — influences the network learning regime. Through both empirical and theoretical analyses, we discover that high-rank initializations typically yield smaller network changes indicative of lazier learning, a finding we also confirm with experimentally-driven initial connectivity in recurrent neural networks. Conversely, low-rank initialization biases learning towards richer learning. Importantly, however, as an exception to this rule, we find lazier learning can still occur with a low-rank initialization that aligns with task and data statistics. Our research highlights the pivotal role of initial weight structures in shaping learning regimes, with implications for metabolic costs of plasticity and risks of catastrophic forgetting.

2024-01-16

ICLR.cc/2024/Conference (poster)

Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency

Tianhong Li

Sangnie Bhardwaj

Yonglong Tian

Han Zhang

Jarred Barber

Dina Katabi

Huiwen Chang

Dilip Krishnan

Current vision-language generative models rely on expansive corpora of paired image-text data to attain optimal performance and generalizati… (voir plus)on capabilities. However, automatically collecting such data (e.g. via large-scale web scraping) leads to low quality and poor image-text correlation, while human annotation is more accurate but requires significant manual effort and expense. We introduce

2024-01-16

ICLR.cc/2024/Conference (spotlight)

Sufficient conditions for offline reactivation in recurrent neural networks

Nanda H Krishna

Colin Bredenberg

Daniel Levenstein

Blake Richards

During periods of quiescence, such as sleep, neural activity in many brain circuits resembles that observed during periods of task engagemen… (voir plus)t. However, the precise conditions under which task-optimized networks can autonomously reactivate the same network states responsible for online behavior is poorly understood. In this study, we develop a mathematical framework that outlines sufficient conditions for the emergence of neural reactivation in circuits that encode features of smoothly varying stimuli. We demonstrate mathematically that noisy recurrent networks optimized to track environmental state variables using change-based sensory information naturally develop denoising dynamics, which, in the absence of input, cause the network to revisit state configurations observed during periods of online activity. We validate our findings using numerical experiments on two canonical neuroscience tasks: spatial position estimation based on self-motion cues, and head direction estimation based on angular velocity cues. Overall, our work provides theoretical support for modeling offline reactivation as an emergent consequence of task optimization in noisy neural circuits.

2024-01-16

ICLR.cc/2024/Conference (poster)