Publications

Mutations associated with neuropsychiatric conditions delineate functional brain connectivity dimensions contributing to autism and schizophrenia

Clara A. Moreau

Sebastian G. W. Urchs

Kumar Kuldeep

Pierre Orban

Catherine Schramm

Guillaume Dumas

Aurélie Labbe

Guillaume Huguet

Elise Douard

Pierre-Olivier Quirion

Amy Lin

Leila Kushan

Stephanie Grot

David Luck

Adrianna Mendrek

Stephane Potvin

Emmanuel Stip

Thomas Bourgeron

Alan C. Evans

Carrie E. Bearden … (see 2 more)

Lune Bellec

Sébastien Jacquemont

2020-10-19

Nature Communications (published)

doi.org

Neural Function Modules with Sparse Arguments: A Dynamic Approach to Integrating Information across Layers

Alex Lamb

Anirudh Goyal

A. Slowik

Michael Curtis Mozer

Philippe Beaudoin

Yoshua Bengio

Feed-forward neural networks consist of a sequence of layers, in which each layer performs some processing on the information from the previ… (see more)ous layer. A downside to this approach is that each layer (or module, as multiple modules can operate in parallel) is tasked with processing the entire hidden state, rather than a particular part of the state which is most relevant for that module. Methods which only operate on a small number of input variables are an essential part of most programming languages, and they allow for improved modularity and code re-usability. Our proposed method, Neural Function Modules (NFM), aims to introduce the same structural capability into deep learning. Most of the work in the context of feed-forward networks combining top-down and bottom-up feedback is limited to classification problems. The key contribution of our work is to combine attention, sparsity, top-down and bottom-up feedback, in a flexible algorithm which, as we show, improves the results in standard classification, out-of-domain generalization, generative modeling, and learning representations in the context of reinforcement learning.

2020-10-15

ArXiv (preprint)

arxiv.org

Parametric models for combined failure time data from an incident cohort study and a prevalent cohort study with follow-up

James H. McVittie

David B. Wolfson

David A. Stephens

Vittorio Addona

David Buckeridge

2020-10-12

The International Journal of Biostatistics (published)

doi.org

GraphMix: Improved Training of GNNs for Semi-Supervised Learning

Vikas Verma

Meng Qu

Kenji Kawaguchi

Alex Lamb

Yoshua Bengio

Juho Kannala

Jian Tang

We present GraphMix, a regularization method for Graph Neural Network based semi-supervised object classification, whereby we propose to tra… (see more)in a fully-connected network jointly with the graph neural network via parameter sharing and interpolation-based regularization. Further, we provide a theoretical analysis of how GraphMix improves the generalization bounds of the underlying graph neural network, without making any assumptions about the "aggregation" layer or the depth of the graph neural networks. We experimentally validate this analysis by applying GraphMix to various architectures such as Graph Convolutional Networks, Graph Attention Networks and Graph-U-Net. Despite its simplicity, we demonstrate that GraphMix can consistently improve or closely match state-of-the-art performance using even simpler architectures such as Graph Convolutional Networks, across three established graph benchmarks: Cora, Citeseer and Pubmed citation network datasets, as well as three newly proposed datasets: Cora-Full, Co-author-CS and Co-author-Physics.

2020-10-11

AAAI Conference on Artificial Intelligence (published)

doi.org

A Fully Tensorized Recurrent Neural Network

Charles Onu

Jacob Miller

Doina Precup

2020-10-08

ArXiv (preprint)

arxiv.org

HyPyP: a Hyperscanning Python Pipeline for inter-brain connectivity analysis

Anaël Ayrolles

Florence Brun

Phoebe Chen

Amir Djalovski

Yann Beauxis

Richard Delorme

Thomas Bourgeron

Suzanne Dikker

Guillaume Dumas

Abstract The bulk of social neuroscience takes a ‘stimulus-brain’ approach, typically comparing brain responses to different types of so… (see more)cial stimuli, but most of the time in the absence of direct social interaction. Over the last two decades, a growing number of researchers have adopted a ‘brain-to-brain’ approach, exploring similarities between brain patterns across participants as a novel way to gain insight into the social brain. This methodological shift has facilitated the introduction of naturalistic social stimuli into the study design (e.g. movies) and, crucially, has spurred the development of new tools to directly study social interaction, both in controlled experimental settings and in more ecologically valid environments. Specifically, ‘hyperscanning’ setups, which allow the simultaneous recording of brain activity from two or more individuals during social tasks, has gained popularity in recent years. However, currently, there is no agreed-upon approach to carry out such ‘inter-brain connectivity analysis’, resulting in a scattered landscape of analysis techniques. To accommodate a growing demand to standardize analysis approaches in this fast-growing research field, we have developed Hyperscanning Python Pipeline, a comprehensive and easy open-source software package that allows (social) neuroscientists to carry-out and to interpret inter-brain connectivity analyses.

2020-10-08

Social Cognitive and Affective Neuroscience (published)

doi.org

Inference and Prediction Diverge in Biomedicine

Danilo Bzdok

Denis-Alexander Engemann

Bertrand Thirion

2020-10-08

Patterns (published)

doi.org

Signal diffusion along connectome gradients and inter-hub routing differentially contribute to dynamic human brain function

Bo-yong Park

Reinder Vos de Wael

Casey Paquola

Sara Lariviere

Oualid Benkarim

Jessica Royer

Shahin Tavakol

Raul Rodriguez-cruces

Qiongling Li

Sofie Valk

Daniel S. Margulies

Bratislav Mišić

Danilo Bzdok

Jonathan Smallwood

Boris C Bernhardt

2020-10-07

NeuroImage (published)

doi.org

A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix

Thang Doan

Mehdi Abbana Bennani

Bogdan Mazoure

Guillaume Rabusseau

Pierre Alquier

Continual learning (CL) is a setting in which an agent has to learn from an incoming stream of data during its entire lifetime. Although maj… (see more)or advances have been made in the field, one recurring problem which remains unsolved is that of Catastrophic Forgetting (CF). While the issue has been extensively studied empirically, little attention has been paid from a theoretical angle. In this paper, we show that the impact of CF increases as two tasks increasingly align. We introduce a measure of task similarity called the NTK overlap matrix which is at the core of CF. We analyze common projected gradient algorithms and demonstrate how they mitigate forgetting. Then, we propose a variant of Orthogonal Gradient Descent (OGD) which leverages structure of the data through Principal Component Analysis (PCA). Experiments support our theoretical findings and show how our method reduces CF on classical CL datasets.

2020-10-07

ArXiv (preprint)

arxiv.org

Contact Graph Epidemic Modelling of COVID-19 for Transmission and Intervention Strategies

Abby Leung

Xiaoye Ding

Shenyang Huang

Reihaneh Rabbany

The coronavirus disease 2019 (COVID-19) pandemic has quickly become a global public health crisis unseen in recent years. It is known that t… (see more)he structure of the human contact network plays an important role in the spread of transmissible diseases. In this work, we study a structure aware model of COVID-19 CGEM. This model becomes similar to the classical compartment-based models in epidemiology if we assume the contact network is a Erdos-Renyi (ER) graph, i.e. everyone comes into contact with everyone else with the same probability. In contrast, CGEM is more expressive and allows for plugging in the actual contact networks, or more realistic proxies for it. Moreover, CGEM enables more precise modelling of enforcing and releasing different non-pharmaceutical intervention (NPI) strategies. Through a set of extensive experiments, we demonstrate significant differences between the epidemic curves when assuming different underlying structures. More specifically we demonstrate that the compartment-based models are overestimating the spread of the infection by a factor of 3, and under some realistic assumptions on the compliance factor, underestimating the effectiveness of some of NPIs, mischaracterizing others (e.g. predicting a later peak), and underestimating the scale of the second peak after reopening.

2020-10-06

ArXiv (preprint)

arxiv.org