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

Catherine Bouchard

Doctorat - Université Laval

Cynthia García Ybarra

Maîtrise recherche - Université Laval

Sara Karami

Doctorat - Université Laval

Benjamin Léger

Doctorat - 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

Hessian Aware Low-Rank Weight Perturbation for Continual Learning

Jiaqi Li

Yuanhao Lai

Rui Wang

Changjian Shui

Sabyasachi Sahoo

Charles Ling

Shichun Yang

Boyu Wang

Fan Zhou

Continual learning aims to learn a series of tasks sequentially without forgetting the knowledge acquired from the previous ones. In this wo… (voir plus)rk, we propose the Hessian Aware Low-Rank Perturbation algorithm for continual learning. By modeling the parameter transitions along the sequential tasks with the weight matrix transformation, we propose to apply the low-rank approximation on the task-adaptive parameters in each layer of the neural networks. Specifically, we theoretically demonstrate the quantitative relationship between the Hessian and the proposed low-rank approximation. The approximation ranks are then globally determined according to the marginal increment of the empirical loss estimated by the layer-specific gradient and low-rank approximation error. Furthermore, we control the model capacity by pruning less important parameters to diminish the parameter growth. We conduct extensive experiments on various benchmarks, including a dataset with large-scale tasks, and compare our method against some recent state-of-the-art methods to demonstrate the effectiveness and scalability of our proposed method. Empirical results show that our method performs better on different benchmarks, especially in achieving task order robustness and handling the forgetting issue. The source code is at https://github.com/lijiaqi/HALRP.

2024-01-01

IEEE Trans. Knowl. Data Eng. (publié)

Filtering Pixel Latent Variables for Unmixing Noisy and Undersampled Volumetric Images

Catherine Bouchard

Andréanne Deschênes

Vincent Boulanger

Jean-Michel Bellavance

Julia Chabbert

Alexy Pelletier-Rioux

Flavie Lavoie-Cardinal

2023-12-08

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

Catherine Bouchard

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

Catherine Bouchard

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

Catherine Bouchard

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.,

Lifelong Online Learning from Accumulated Knowledge

Changjian Shui

William Wang

Ihsen Hedhli

Chi Man Wong

Feng Wan

Boyu Wang

In this article, we formulate lifelong learning as an online transfer learning procedure over consecutive tasks, where learning a given task… (voir plus) depends on the accumulated knowledge. We propose a novel theoretical principled framework, lifelong online learning, where the learning process for each task is in an incremental manner. Specifically, our framework is composed of two-level predictions: the prediction information that is solely from the current task; and the prediction from the knowledge base by previous tasks. Moreover, this article tackled several fundamental challenges: arbitrary or even non-stationary task generation process, an unknown number of instances in each task, and constructing an efficient accumulated knowledge base. Notably, we provide a provable bound of the proposed algorithm, which offers insights on the how the accumulated knowledge improves the predictions. Finally, empirical evaluations on both synthetic and real datasets validate the effectiveness of the proposed algorithm.

2022-10-17

ACM Transactions on Knowledge Discovery from Data (publié)

The 5-year longitudinal diagnostic profile and health services utilization of patients treated with electroconvulsive therapy in Quebec: a population-based study

Simon Lafrenière

Fatemeh Gholi-Zadeh-Kharrat

Caroline Sirois

Victoria Massamba

Louis Rochette

Camille Brousseau-Paradis

Simon Patry

Morgane Lemasson

Geneviève Gariépy

Chantal Mérette

Elham Rahme

Alain Lesage

2022-09-26

Social Psychiatry and Psychiatric Epidemiology (publié)