Guillaume Dumas

Zahra Sheikhbahaee

Postdoctorat - UdeM

Co-superviseur⋅e :

Github

Pascal Tikeng Notsawo

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Postdoctorat - UdeM

Publications

Introducing Brain Foundation Models

Mohammad Javad Darvishi Bayazi

Hena Ghonia

Roland Riachi

Bruno Aristimunha

Arian Khorasani

Md Rifat Arefin

Amin Darabi

Sylvain Chevallier

Brain function represents one of the most complex systems driving our world. Decoding its signals poses significant challenges, particularly… (voir plus) due to the limited availability of data and the high cost of recordings. The existence of large hospital datasets and laboratory collections partially mitigates this issue. However, the lack of standardized recording protocols, varying numbers of channels, diverse setups, scenarios, and recording devices further complicate the task. This work addresses these challenges by introducing the Brain Foundation Model (BFM), a suite of open-source models trained on brain signals. These models serve as foundational tools for various types of time-series neuroimaging tasks. This work presents the first model of the BFM series, which is trained on electroencephalogram signal data. Our results demonstrate that BFM-EEG can generate signals more accurately than other models. Upon acceptance, we will release the model weights and pipeline.

2024-10-10

NeurIPS.cc/2024/Workshop/TSALM (publié)

LLMs and Personalities: Inconsistencies Across Scales

Tosato Tommaso

Mahmood Hegazy

David Lemay

Mohammed Abukalam

This study investigates the application of human psychometric assessments to large language models (LLMs) to examine their consistency and m… (voir plus)alleability in exhibiting personality traits. We administered the Big Five Inventory (BFI) and the Eysenck Personality Questionnaire-Revised (EPQ-R) to various LLMs across different model sizes and persona prompts. Our results reveal substantial variability in responses due to question order shuffling, challenging the notion of a stable LLM "personality." Larger models demonstrated more consistent responses, while persona prompts significantly influenced trait scores. Notably, the assistant persona led to more predictable scaling, with larger models exhibiting more socially desirable and less variable traits. In contrast, non-conventional personas displayed unpredictable behaviors, sometimes extending personality trait scores beyond the typical human range. These findings have important implications for understanding LLM behavior under different conditions and reflect on the consequences of scaling.

2024-10-09

NeurIPS.cc/2024/Workshop/Behavioral_ML (présentation orale)

LLMs and Personalities: Inconsistencies Across Scales

Tosato Tommaso

Mahmood Hegazy

David Lemay

Mohammed Abukalam

2024-10-09

NeurIPS.cc/2024/Workshop/Behavioral_ML (présentation orale)

Scalable Approaches for a Theory of Many Minds

Maximilian Puelma Touzel

Amin Memarian

Matthew D Riemer

Andrei Mircea

Andrew Robert Williams

Elin Ahlstrand

Lucas Lehnert

Rupali Bhati

A major challenge as we move towards building agents for real-world problems, which could involve a massive number of human and/or machine a… (voir plus)gents, is that we must learn to reason about the behavior of these many other agents. In this paper, we consider the problem of scaling a predictive Theory of Mind (ToM) model to a very large number of interacting agents with a fixed computational budget. Motivated by the limited diversity of agent types, existing approaches to scalable TOM learn versatile single-agent representations for quickly adapting to new agents encountered sequentially. We consider the more general setting that many agents are observed in parallel and formulate the corresponding Theory of Many Minds (ToMM) problem of estimating the joint policy. We frame the scaling behavior of solutions in terms of parameter sharing schemes and in particular propose two parameter-free architectural features that endow models with the ability to exploit action correlations: encoding a multi-agent context, and decoding through an abstracted joint action space. The increased predictive capabilities that have come with foundation models have made it easier to imagine the possibility of using these models to make simulations that imitate the behavior of many agents within complex real-world systems. Being able to perform these simulations in a general-purpose way would not only help make more capable agents, it also would be a very useful capability for applications in social science, political science, and economics.

2024-06-18

ICML.cc/2024/Workshop/Agentic_Markets (poster)

Lost in Translation: The Algorithmic Gap Between LMs and the Brain

Tosato Tommaso

Tikeng Notsawo Pascal Junior

Helbling Saskia

Language Models (LMs) have achieved impressive performance on various linguistic tasks, but their relationship to human language processing … (voir plus)in the brain remains unclear. This paper examines the gaps and overlaps between LMs and the brain at different levels of analysis, emphasizing the importance of looking beyond input-output behavior to examine and compare the internal processes of these systems. We discuss how insights from neuroscience, such as sparsity, modularity, internal states, and interactive learning, can inform the development of more biologically plausible language models. Furthermore, we explore the role of scaling laws in bridging the gap between LMs and human cognition, highlighting the need for efficiency constraints analogous to those in biological systems. By developing LMs that more closely mimic brain function, we aim to advance both artificial intelligence and our understanding of human cognition.

2024-06-17

ICML.cc/2024/Workshop/LLMs_and_Cognition (poster)

295. Rare Variant Genetic Architecture of the Human Cortical MRI Phenotypes in General Population

Kuldeep Kumar

Sayeh Kazem

Zhijie Liao

Jakub Kopal

Guillaume Huguet

Thomas Renne

Martineau Jean-Louis

Zhe Xie

Zohra Saci

Laura Almasy

David C. Glahn

Tomas Paus

Carrie Bearden

Paul Thompson

Richard A.I. Bethlehem

Varun Warrier

Sébastien Jacquemont

2024-05-01

Biological Psychiatry (publié)

Effects of gene dosage on cognitive ability: A function-based association study across brain and non-brain processes

Guillaume Huguet

Thomas Renne

Cécile Poulain

Alma Dubuc

Kuldeep Kumar

Sayeh Kazem

Worrawat Engchuan

Omar Shanta

Elise Douard

Catherine Proulx

Martineau Jean-Louis

Zohra Saci

Josephine Mollon

Laura Schultz

Emma E M Knowles

Simon R. Cox

David Porteous

Gail Davies

Paul Redmond

Sarah E. Harris … (voir 10 de plus)

Gunter Schumann

Aurélie Labbe

Zdenka Pausova

Tomas Paus

Stephen W Scherer

Jonathan Sebat

Laura Almasy

David C. Glahn

Sébastien Jacquemont

2024-04-17

bioRxiv (prépublication)

Predicting Grokking Long Before it Happens: A look into the loss landscape of models which grok

Tikeng Notsawo Pascal Junior

Pascal Notsawo

Hattie Zhou

Mohammad Pezeshki

2024-03-04

ICLR.cc/2024/Workshop/ME-FoMo (poster)

Sources of richness and ineffability for phenomenally conscious states

Xu Ji

Eric Elmoznino

George Deane

Axel Constant

Guillaume Lajoie

Jonathan Simon

Yoshua Bengio

2024-03-01

Neuroscience of Consciousness (publié)

arxiv.org

The « jingle-jangle fallacy » of empathy: Delineating affective, cognitive and motor components of empathy from behavioral synchrony using a virtual agent

Julia Ayache

Alexander Sumich

D. Kuss

Darren Rhodes

Nadja Heym

2024-03-01

Personality and Individual Differences (publié)

Effective Latent Differential Equation Models via Attention and Multiple Shooting

Germán Abrevaya

Mahta Ramezanian-Panahi

Jean-Christophe Gagnon-Audet

Pablo Polosecki

Silvina Ponce Dawson

Guillermo Cecchi

2024-02-26

TMLR (accepté)

From physics to sentience: Deciphering the semantics of the free-energy principle and evaluating its claims: Comment on "Path integrals, particular kinds, and strange things" by Karl Friston et al.

Zahra Sheikhbahaee

Adam Safron

Casper Hesp

2023-12-01

Physics of Life Reviews (publié)