Publications

Monocular Robot Navigation with Self-Supervised Pretrained Vision Transformers

Miguel Saavedra-Ruiz

Sacha Morin

In this work, we consider the problem of learning a perception model for monocular robot navigation using few annotated images. Using a Visi… (voir plus)on Transformer (ViT) pretrained with a label-free self-supervised method, we successfully train a coarse image segmentation model for the Duckietown environment using 70 training images. Our model performs coarse image segmentation at the

2022-03-07

ArXiv (preprint)

doi.org

arxiv.org

Feeding What You Need by Understanding What You Learned

Xiaoqiang Wang

Bang Liu

Fangli Xu

Bowei Long

Siliang Tang

Lingfei Wu

2022-03-05

ArXiv (prépublication)

doi.org

arxiv.org

A New Era: Intelligent Tutoring Systems Will Transform Online Learning for Millions

Francois St-Hilaire

Dung D. Vu

Antoine Frau

Nathan J. Burns

Farid Faraji

Joseph Potochny

Stephane Robert

Arnaud Roussel

Selene Zheng

Taylor Glazier

Junfel Vincent Romano

Robert Belfer

Muhammad Shayan

Ariella Smofsky

Tommy Delarosbil

Seulmin Ahn

Simon Eden-Walker

Kritika Sony

Ansona Onyi Ching

Sabina Elkins … (voir 11 de plus)

A. Stepanyan

Adela Matajova

Victor Chen

Hossein Sahraei

Robert Larson

N. Markova

Andrew Barkett

Laurent Charlin

Yoshua Bengio

Iulian V. Serban

Ekaterina Kochmar

2022-03-03

ArXiv (prépublication)

doi.org

arxiv.org

Application of AI in community based primary health care: Systematic review and critical appraisal

Samira Abbasgholizadeh-Rahimi

Patrick Archambault

Hervé Tchala Vignon Zomahoun

Sam Chandavong

Marie-Pierre Gagnon

Sabrina M. Wong

Gauri Sharma

Lyse Langlois

Nathalie Rheault

Yves Couturier

Jean Légaré

2022-03-01

Systematic review, meta-analysis, or scoping review (publié)

doi.org

Cross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity

Jingwei Li

Danilo Bzdok

Jianzhong Chen

Angela Tam

Leon Qi

Rong Ooi

Avram J. Holmes

Tian Ge

K. Patil

M. Jabbi

Simon B. Eickhoff

B.T. Thomas Yeo

Sarah Genon

Algorithmic biases that favor majority populations pose a key challenge to the application of machine learning for precision medicine. Here,… (voir plus) we assessed such bias in prediction models of behavioral phenotypes from brain functional magnetic resonance imaging. We examined the prediction bias using two independent datasets (preadolescent versus adult) of mixed ethnic/racial composition. When predictive models were trained on data dominated by white Americans (WA), out-of-sample prediction errors were generally higher for African Americans (AA) than for WA. This bias toward WA corresponds to more WA-like brain-behavior association patterns learned by the models. When models were trained on AA only, compared to training only on WA or an equal number of AA and WA participants, AA prediction accuracy improved but stayed below that for WA. Overall, the results point to the need for caution and further research regarding the application of current brain-behavior prediction models in minority populations.

2022-03-01

Science Advances (published)

doi.org

Enjeux éthiques de l’IA en santé - Fiche 4

Joé T. Martineau

Frédérique Romy Godin

Janine Badr

Alexandre Castonguay

Martin Cousineau

Philippe Després

Aude Motulsky

Jean Noel Nikiema

Cécile Petitgand

Catherine Régis

La présente fiche propose une revue des différents enjeux éthiques liés au développement et à l’utilisation des technologies d’int… (voir plus)elligence artificielle dans le milieu de la santé, en trois parties. D’abord, nous aborderons les enjeux éthiques liés à l’exploitation de données massives nécessaires à l’entrainement des algorithmes de l’IA. Ensuite, nous présenterons les principaux enjeux éthiques liés au développement et à l’utilisation des SIA en santé, en abordant la façon dont ces systèmes impactent nos vies ainsi que l’environnement physique et social dans lequel nous vivons. Nous présenterons finalement les principales initiatives nationales et internationales en matière d’éthique de l’IA et de la gestion des données, fruits et reflets d’une réflexion globale sur ces sujets. Ces initiatives ont notamment proposé des lignes directrices et principes normatifs servant de guides pour le développement de technologies de l’IA éthiques et responsables Il s'agit de la quatrième fiche d'une série de 4 développée dans le cadre d'un mandat réalisé pour le Ministère de la Santé et des Services sociaux du Québec (MSSS).

2022-03-01

(publié)

doi.org

Guidelines for the Computational Testing of Machine Learning approaches to Vehicle Routing Problems

Luca Accorsi

Andrea Lodi

Daniele Vigo

2022-03-01

Operations Research Letters (publié)

doi.org

arxiv.org

Interindividual Differences in Cortical Thickness and Their Genomic Underpinnings in Autism Spectrum Disorder.

Christine Ecker

Charlotte M. Pretzsch

Anke Bletsch

Caroline Mann

Tim Schaefer

Sara Ambrosino

Julian Tillmann

Afsheen Yousaf

Andreas Chiocchetti

Michael V. Lombardo

Varun Warrier

Nico Bast

Carolin Moessnang

Sarah Baumeister

Flavio Dell’Acqua

Dorothea L. Floris

Mariam Zabihi

Andre Marquand

Freddy Cliquet

Claire Leblond … (voir 19 de plus)

Clara A. Moreau

Nick Puts

Tobias Banaschewski

Emily J. H. Jones

Luke Mason

Sven Bölte

Andreas Meyer-Lindenberg

Antonio Persico

Sarah Durston

Simon Baron-Cohen

Will Spooren

Eva Loth

Christine M. Freitag

Tony Charman

Guillaume Dumas

Thomas Bourgeron

Christian Beckmann

Jan K. Buitelaar

Declan Murphy

2022-03-01

American Journal of Psychiatry (publié)

doi.org

JANOS: An Integrated Predictive and Prescriptive Modeling Framework

David Bergman

Teng Huang

Philip Brooks

Andrea Lodi

Arvind U. Raghunathan

Business research practice is witnessing a surge in the integration of predictive modeling and prescriptive analysis. We describe a modeling… (voir plus) framework JANOS that seamlessly integrates the two streams of analytics, allowing researchers and practitioners to embed machine learning models in an end-to-end optimization framework. JANOS allows for specifying a prescriptive model using standard optimization modeling elements such as constraints and variables. The key novelty lies in providing modeling constructs that enable the specification of commonly used predictive models within an optimization model, have the features of the predictive model as variables in the optimization model, and incorporate the output of the predictive models as part of the objective. The framework considers two sets of decision variables: regular and predicted. The relationship between the regular and the predicted variables is specified by the user as pretrained predictive models. JANOS currently supports linear regression, logistic regression, and neural network with rectified linear activation functions. In this paper, we demonstrate the flexibility of the framework through an example on scholarship allocation in a student enrollment problem and provide a numeric performance evaluation. Summary of Contribution. This paper describes a new software tool, JANOS, that integrates predictive modeling and discrete optimization to assist decision making. Specifically, the proposed solver takes as input user-specified pretrained predictive models and formulates optimization models directly over those predictive models by embedding them within an optimization model through linear transformations.

2022-03-01

INFORMS Journal on Computing (publié)

doi.org

arxiv.org

Multistep networks for roll force prediction in hot strip rolling mill

Shuh-Rong Shen

Denzel Guye

Xiaoping Ma

S. Yue

Narges Armanfard

2022-03-01

Machine Learning with Applications (published)