Christian Gagné

Biographie

Christian Gagné est professeur au Département de génie électrique et de génie informatique de l’Université Laval depuis 2008, et dirige l’Institut intelligence et données (IID). Il détient une chaire en IA Canada-CIFAR et est membre associé à Mila – Institut québécois d’intelligence artificielle. Il est également membre du Laboratoire de vision et systèmes numériques (LVSN), une composante du Centre de recherche en robotique, vision et intelligence machine (CeRVIM) ainsi que du Centre de recherche en données massives (CRDM) de l’Université Laval. Il fait partie des regroupements stratégiques REPARTI et UNIQUE du Fonds de recherche du Québec – Nature et technologies (FRQNT), du centre VITAM du Fonds de recherche du Québec – Santé (FRQS) et de l’Observatoire international sur les impacts sociétaux de l’IA et du numérique (OBVIA).

Ses intérêts de recherche portent sur l’élaboration de méthodes pour l’apprentissage automatique et l’optimisation stochastique. En particulier, il se consacre aux réseaux de neurones profonds, à l’apprentissage et au transfert de représentations, au méta-apprentissage ainsi qu’à l’apprentissage multitâche. Il s’intéresse également aux approches d’optimisation basées sur des modèles probabilistes ainsi qu’aux algorithmes évolutionnaires, entre autres pour l’optimisation boîte noire et la programmation automatique. Une part importante de ses travaux porte également sur la mise en pratique de ces techniques dans des domaines comme la vision numérique, la microscopie, la santé, l’énergie et les transports.

Étudiants actuels

Frédéric Beaupré

Doctorat - Université Laval

Doctorat - Université Laval

Olivier Bussière

Maîtrise recherche - Université Laval

olivier2202@gmail.com

Cynthia García Ybarra

Maîtrise recherche - Université Laval

Sara Karami

Doctorat - Université Laval

Benjamin Léger

Doctorat - Université Laval

Samy Mammeri

Stagiaire de recherche - Université Laval

Jonas Ngnawe

Doctorat - Université Laval

Fatemeh Nourilenjan Nokabadi

Doctorat - Université Laval

Doctorat - Université Laval

Adam Tupper

Doctorat - Université Laval

adam.tupper.1@ulaval.ca

Publications

Unmixing Optical Signals from Undersampled Volumetric Measurements by Filtering the Pixel Latent Variables

Andréanne Deschênes

Vincent Boulanger

Jean-Michel Bellavance

Julia Chabbert

Alexy Pelletier-Rioux

Flavie Lavoie-Cardinal

The development of signal unmixing algorithms is essential for leveraging multimodal datasets acquired through a wide array of scientific im… (voir plus)aging technologies, including hyperspectral or time-resolved acquisitions. In experimental physics, enhancing the spatio-temporal resolution or expanding the number of detection channels often leads to diminished sampling rate and signal-to-noise ratio (SNR), significantly affecting the efficacy of signal unmixing algorithms. We propose Latent Unmixing, a new approach which applies band-pass filters to the latent space of a multi-dimensional convolutional neural network to disentangle overlapping signal components. It enables better isolation and quantification of individual signal contributions, especially in the context of undersampled distributions. Using multi-dimensional convolution kernels to process all dimensions simultaneously enhances the network's ability to extract information from adjacent pixels, and time- or spectral-bins. This approach enables more effective separation of components in cases where individual pixels do not provide clear, well-resolved information. We showcase the method's practical use in experimental physics through two test cases that highlight the versatility of our approach: fluorescence lifetime microscopy and mode decomposition in optical fibers. The latent unmixing method extracts valuable information from complex signals that cannot be resolved by standard methods. It opens new possibilities in optics and photonics for multichannel separations at an increased sampling rate.

2023-12-08

ArXiv (prépublication)

Unmixing Optical Signals from Undersampled Volumetric Measurements by Filtering the Pixel Latent Variables

Andréanne Deschênes

Vincent Boulanger

Jean-Michel Bellavance

Julia Chabbert

Alexy Pelletier-Rioux

Flavie Lavoie-Cardinal

2023-12-08

ArXiv (prépublication)

Hessian Aware Low-Rank Perturbation for Order-Robust Continual Learning

Jiaqi Li

Rui Wang

Yuanhao Lai

Changjian Shui

Sabyasachi Sahoo

Charles Ling

Shichun Yang

Boyu Wang

Fan Zhou

2023-11-26

ArXiv (prépublication)

Hessian Aware Low-Rank Perturbation for Order-Robust Continual Learning

Jiaqi Li

Rui Wang

Yuanhao Lai

Changjian Shui

Sabyasachi Sahoo

Charles Ling

Shichun Yang

Boyu Wang

Fan Zhou

2023-11-26

ArXiv (prépublication)

Towards More General Loss and Setting in Unsupervised Domain Adaptation

Changjian Shui

Ruizhi Pu

Gezheng Xu

Jun Wen

Fan Zhou

Charles Ling

Boyu Wang

In this article, we present an analysis of unsupervised domain adaptation with a series of theoretical and algorithmic results. We derive a … (voir plus)novel Rényi-

2023-10-01

IEEE Transactions on Knowledge and Data Engineering (publié)

Resolution enhancement with a task-assisted GAN to guide optical nanoscopy image analysis and acquisition

Theresa Wiesner

Andréanne Deschênes

Anthony Bilodeau

Benoit Turcotte

Flavie Lavoie-Cardinal

2023-07-27

Nature Machine Intelligence (publié)

Domain Agnostic Image-to-image Translation using Low-Resolution Conditioning

Mohamed Abderrahmen Abid

Arman Afrasiyabi

Ihsen Hedhli

Jean‐François Lalonde

2023-05-08

ArXiv (prépublication)

Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output Codes

Thomas Philippon

Neural network ensembles have been studied extensively in the context of adversarial robustness and most ensemble-based approaches remain vu… (voir plus)lnerable to adaptive attacks. In this paper, we investigate the robustness of Error-Correcting Output Codes (ECOC) ensembles through architectural improvements and ensemble diversity promotion. We perform a comprehensive robustness assessment against adaptive attacks and investigate the relationship between ensemble diversity and robustness. Our results demonstrate the benefits of ECOC ensembles for adversarial robustness compared to regular ensembles of convolutional neural networks (CNNs) and show why the robustness of previous implementations is limited. We also propose an adversarial training method specific to ECOC ensembles that allows to further improve robustness to adaptive attacks.

2023-03-04

ArXiv (prépublication)

A case–control study on predicting population risk of suicide using health administrative data: a research protocol

JianLi Wang

Fatemeh Gholi Zadeh Kharrat

Jean-François Pelletier

Louis Rochette

Eric Pelletier

Pascale Lévesque

Victoria Massamba

Camille Brousseau-Paradis

Mada Mohammed

Geneviève Gariépy

Alain Lesage

2023-02-27

BMJ Open (publié)

Filtering Pixel Latent Variables for Unmixing Volumetric Images

Vincent Boulanger

Flavie Lavoie-Cardinal

Measurements of different overlapping components require robust unmixing algorithms to convert the raw multi-dimensional measurements to use… (voir plus)ful unmixed images. Such algorithms perform reliable separation of the components when the raw signal is fully resolved and contains enough information to fit curves on the raw distributions. In experimental physics, measurements are often noisy, undersam-pled, or unresolved spatially or spectrally. We propose a novel method where bandpass filters are applied to the latent space of a multi-dimensional convolutional neural network to separate the overlapping signal components and extract each of their relative contributions. Simultaneously processing all dimensions with multi-dimensional convolution kernels empowers the network to combine the information from adjacent pixels and time-or spectral-bins, facilitating component separation in instances where individual pixels lack well-resolved information. We demonstrate the applicability of the method to real experimental physics problems using fluorescence lifetime microscopy and mode decomposition in optical fibers as test cases. The successful application of our approach to these two distinct experimental cases, characterized by different measured distributions, highlights the versatility of our approach in addressing a wide array of imaging tasks.

2023-01-01

arXiv.org (preprint)

Filtering Pixel Latent Variables for Unmixing Volumetric Images

Vincent Boulanger

Flavie Lavoie-Cardinal

2023-01-01

arXiv.org (prépublication)

Gap Minimization for Knowledge Sharing and Transfer

Boyu Wang

Jorge A. Mendez

Changjian Shui

Fan Zhou

Di Wu

Gezheng Xu

Eric R. Eaton

Learning from multiple related tasks by knowledge sharing and transfer has become increasingly relevant over the last two decades. In order … (voir plus)to successfully transfer information from one task to another, it is critical to understand the similarities and differences between the domains. In this paper, we introduce the notion of \emph{performance gap}, an intuitive and novel measure of the distance between learning tasks. Unlike existing measures which are used as tools to bound the difference of expected risks between tasks (e.g.,