Christian Gagné

Biography

Christian Gagné has been a professor in the Department of Electrical and Computer Engineering at Université Laval since 2008.

He is the director of the Institute Intelligence and Data (IID), holds a Canada CIFAR AI Chair, and is an associate member of Mila – Quebec Artificial Intelligence Institute.

Gagné is also a member of Université Laval’s Computer Vision and Systems Laboratory (LVSN), as well as its Robotics, Vision and Machine Intelligence Research Centre (CeRVIM) and its Big Data Research Centre (CRDM). He is a member of the REPARTI and UNIQUE strategic clusters of the FRQNT, the VITAM centre of the FRQS, and the International Observatory on the Societal Impacts of AI and Digital Technologies (OBVIA).

Gagné’s research focuses on the development of methods for machine learning and stochastic optimization. In particular, he is interested in deep neural networks, representation learning and transfer, meta-learning and multitasking. He is also interested in optimization approaches based on probabilistic models and evolutionary algorithms, including black-box optimization and automatic programming. An important part of his work is the practical application of these techniques in fields like computer vision, microscopy, healthcare, energy and transportation.

Current Students

Frédéric Beaupré

PhD - Université Laval

Catherine Bouchard

PhD - Université Laval

Olivier Bussière

Master's Research - Université Laval

Sara Karami

PhD - Université Laval

Rose Nayoung Kwon

PhD - Université Laval

Benjamin Léger

PhD - Université Laval

Samy Mammeri

Undergraduate - Université Laval

Jonas Ngnawe

PhD - Université Laval

PhD - Université Laval

Website

Adam Tupper

PhD - Université Laval

Website

Publications

A novel domain adaptation theory with Jensen-Shannon divergence

Qi CHEN

Jun Wen

Fan Zhou

Boyu Wang

2022-08-31

Knowledge-Based Systems (published)

Fair Representation Learning through Implicit Path Alignment

Qi CHEN

Jiaqi Li

Boyu Wang

2022-06-27

Proceedings of the 39th International Conference on Machine Learning (published)

proceedings.mlr.press

Matching Feature Sets for Few-Shot Image Classification

Arman Afrasiyabi

Hugo Larochelle

Jean‐François Lalonde

In image classification, it is common practice to train deep networks to extract a single feature vector per input image. Few-shot classific… (see more)ation methods also mostly follow this trend. In this work, we depart from this established direction and instead propose to extract sets of feature vectors for each image. We argue that a set-based representation intrinsically builds a richer representation of images from the base classes, which can subsequently better transfer to the few-shot classes. To do so, we propose to adapt existing feature extractors to instead produce sets of feature vectors from images. Our approach, dubbed SetFeat, embeds shallow self-attention mechanisms inside existing encoder architectures. The attention modules are lightweight, and as such our method results in encoders that have approximately the same number of parameters as their original versions. During training and inference, a set-to-set matching metric is used to perform image classification. The effectiveness of our proposed architecture and metrics is demonstrated via thorough experiments on standard few-shot datasets-namely miniImageNet, tieredImageNet, and CUB-in both the 1- and 5-shot scenarios. In all cases but one, our method outperforms the state-of-the-art.

2022-06-17

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (published)

arxiv.org

Evolving Domain Generalization

Wei Wang

Gezheng Xu

Ruizhi Pu

Jiaqi Li

Fan Zhou

Charles Ling

Boyu Wang

2022-05-30

ArXiv (preprint)

arxiv.org

Deep learning of chest X-rays can predict mechanical ventilation outcome in ICU-admitted COVID-19 patients

Daniel Gourdeau

Olivier Potvin

Jason Henry Biem

Florence Cloutier

Lyna Abrougui

Patrick Archambault

Carl Chartrand-Lefebvre

Louis Dieumegarde

Louis Gagnon

Raphaelle Giguère

Alexandre Hains

Huy Le

Simon Lemieux

Marie-Hélène Lévesque

Simon Nepveu

Lorne Rosenbloom

An Tang

Issac Yang

Nathalie Duchesne … (see 1 more)

Simon Duchesne

2022-04-12

Scientific Reports (published)

TRACKING AND PREDICTING COVID-19 RADIOLOGICAL TRAJECTORY USING DEEP LEARNING ON CHEST X-RAYS: INITIAL ACCURACY TESTING

Simon Duchesne

Daniel Gourdeau

Patrick Archambault

Carl Chartrand‐Lefebvre

Louis Dieumegarde

Reza Forghani

Alexandre Hains

David Hornstein

H. Le

Simon Lemieux

Marie‐Hélène Lévesque

Diego R. Martín

Lorne Rosenbloom

An Tang

F. Vecchio

Olivier Potvin

Nathalie Duchesne

Decision scores and ethically mindful algorithms are being established to adjudicate mechanical ventilation in the context of potential reso… (see more)urces shortage due to the current onslaught of COVID-19 cases. There is a need for a reproducible and objective method to provide quantitative information for those scores. Towards this goal, we present a retrospective study testing the ability of a deep learning algorithm at extracting features from chest x-rays (CXR) to track and predict radiological evolution. We trained a repurposed deep learning algorithm on the CheXnet open dataset (224,316 chest X-ray images of 65,240 unique patients) to extract features that mapped to radiological labels. We collected CXRs of COVID-19-positive patients from two open-source datasets (last accessed on April 9, 2020)(Italian Society for Medical and Interventional Radiology and MILA). Data collected form 60 pairs of sequential CXRs from 40 COVID patients (mean age ± standard deviation: 56 ± 13 years; 23 men, 10 women, seven not reported) and were categorized in three categories: “Worse”, “Stable”, or “Improved” on the basis of radiological evolution ascertained from images and reports. Receiver operating characteristic analyses, Mann-Whitney tests were performed. On patients from the CheXnet dataset, the area under ROC curves ranged from 0.71 to 0.93 for seven imaging features and one diagnosis. Deep learning features between “Worse” and “Improved” outcome categories were significantly different for three radiological signs and one diagnostic (“Consolidation”, “Lung Lesion”, “Pleural effusion” and “Pneumonia”; all P 0.05). Features from the first CXR of each pair could correctly predict the outcome category between “Worse” and “Improved” cases with 82.7% accuracy. CXR deep learning features show promise for classifying the disease trajectory. Once validated in studies incorporating clinical data and with larger sample sizes, this information may be considered to inform triage decisions.

2022-04-03

Scientific Reports (published)

Active Learning for Capturing Human Decision Policies in a Data Frugal Context

Loïc Grossetête

Alexandre Marois

Bénédicte Chatelais

Daniel Lafond

2022-02-01

Machine Learning, Optimization, and Data Science (published)

On Learning Fairness and Accuracy on Multiple Subgroups

Gezheng Xu

Qi CHEN

Jiaqi Li

Charles Ling

Tal Arbel

Boyu Wang

We propose an analysis in fair learning that preserves the utility of the data while reducing prediction disparities under the criteria of g… (see more)roup sufficiency. We focus on the scenario where the data contains multiple or even many subgroups, each with limited number of samples. As a result, we present a principled method for learning a fair predictor for all subgroups via formulating it as a bilevel objective. Specifically, the subgroup specific predictors are learned in the lower-level through a small amount of data and the fair predictor. In the upper-level, the fair predictor is updated to be close to all subgroup specific predictors. We further prove that such a bilevel objective can effectively control the group sufficiency and generalization error. We evaluate the proposed framework on real-world datasets. Empirical evidence suggests the consistently improved fair predictions, as well as the comparable accuracy to the baselines.

2021-12-31

Advances in Neural Information Processing Systems 35 (NeurIPS 2022) (published)

openreview.net

On the benefits of representation regularization in invariance based domain generalization

Boyu Wang

A crucial aspect of reliable machine learning is to design a deployable system for generalizing new related but unobserved environments. Dom… (see more)ain generalization aims to alleviate such a prediction gap between the observed and unseen environments. Previous approaches commonly incorporated learning the invariant representation for achieving good empirical performance. In this paper, we reveal that merely learning the invariant representation is vulnerable to the related unseen environment. To this end, we derive a novel theoretical analysis to control the unseen test environment error in the representation learning, which highlights the importance of controlling the smoothness of representation. In practice, our analysis further inspires an efficient regularization method to improve the robustness in domain generalization. The proposed regularization is orthogonal to and can be straightforwardly adopted in existing domain generalization algorithms that ensure invariant representation learning. Empirical results show that our algorithm outperforms the base versions in various datasets and invariance criteria.

2021-12-31

Machine Learning (published)

arxiv.org

Neuronal activity remodels the F-actin based submembrane lattice in dendrites but not axons of hippocampal neurons

Flavie Lavoie-Cardinal

Anthony Bilodeau

Mado Lemieux

Marc-André Gardner

Theresa Wiesner

Gabrielle Laramée