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

Research Intern - Université Laval

Jonas Ngnawe

PhD - Université Laval

PhD - Université Laval

Website

Adam Tupper

PhD - Université Laval

Website

Fatemeh Nourilenjan Nokabadi

Publications

Adversarial Bounding Boxes Generation (ABBG) Attack against Visual Object Trackers

Jean-François Lalonde

Adversarial perturbations aim to deceive neural networks into predicting inaccurate results. For visual object trackers, adversarial attacks… (see more) have been developed to generate perturbations by manipulating the outputs. However, transformer trackers predict a specific bounding box instead of an object candidate list, which limits the applicability of many existing attack scenarios. To address this issue, we present a novel white-box approach to attack visual object trackers with transformer backbones using only one bounding box. From the tracker predicted bounding box, we generate a list of adversarial bounding boxes and compute the adversarial loss for those bounding boxes. Experimental results demonstrate that our simple yet effective attack outperforms existing attacks against several robust transformer trackers, including TransT-M, ROMTrack, and MixFormer, on popular benchmark tracking datasets such as GOT-10k, UAV123, and VOT2022STS.

2024-10-14

NeurIPS.cc/2024/Workshop/AdvML-Frontiers (published)

openreview.net

Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers

Frédéric Precioso

Despite extensive research on adversarial training strategies to improve robustness, the decisions of even the most robust deep learning mod… (see more)els can still be quite sensitive to imperceptible perturbations, creating serious risks when deploying them for high-stakes real-world applications. While detecting such cases may be critical, evaluating a model's vulnerability at a per-instance level using adversarial attacks is computationally too intensive and unsuitable for real-time deployment scenarios. The input space margin is the exact score to detect non-robust samples and is intractable for deep neural networks. This paper introduces the concept of margin consistency -- a property that links the input space margins and the logit margins in robust models -- for efficient detection of vulnerable samples. First, we establish that margin consistency is a necessary and sufficient condition to use a model's logit margin as a score for identifying non-robust samples. Next, through comprehensive empirical analysis of various robustly trained models on CIFAR10 and CIFAR100 datasets, we show that they indicate high margin consistency with a strong correlation between their input space margins and the logit margins. Then, we show that we can effectively and confidently use the logit margin to detect brittle decisions with such models. Finally, we address cases where the model is not sufficiently margin-consistent by learning a pseudo-margin from the feature representation. Our findings highlight the potential of leveraging deep representations to assess adversarial vulnerability in deployment scenarios efficiently.

2024-09-24

NeurIPS.cc/2024/Conference (poster)

openreview.net

Quantitative Analysis of Miniature Synaptic Calcium Transients Using Positive Unlabeled Deep Learning

Frédéric Beaupré

Anthony Bilodeau

Theresa Wiesner

Gabriel Leclerc

Mado Lemieux

Gabriel Nadeau

Katrine Castonguay

Bolin Fan

Simon Labrecque

Renée Hložek

Paul De Koninck

Flavie Lavoie-Cardinal

Ca 2+ imaging methods are widely used for studying cellular activity in the brain, allowing detailed ana… (see more)lysis of dynamic processes across various scales. Enhanced by high-contrast optical microscopy and fluorescent Ca 2+ sensors, this technique can be used to reveal localized Ca 2+ fluctuations within neurons, including in sub-cellular compartments, such as the dendritic shaft or spines. Despite advances in Ca 2+ sensors, the analysis of miniature Synaptic Calcium Transients (mSCTs), characterized by variability in morphology and low signal-to-noise ratios, remains challenging. Traditional threshold-based methods struggle with the detection and segmentation of these small, dynamic events. Deep learning (DL) approaches offer promising solutions but are limited by the need for large annotated datasets. Positive Unlabeled (PU) learning addresses this limitation by leveraging unlabeled instances to increase dataset size and enhance performance. This approach is particularly useful in the case of mSCTs that are scarce and small, associated with a very small proportion of the foreground pixels. PU learning significantly increases the effective size of the training dataset, improving model performance. Here, we present a PU learning-based strategy for detecting and segmenting mSCTs. We evaluate the performance of two 3D deep learning models, StarDist-3D and 3D U-Net, which are well established for the segmentation of small volumetric structures in microscopy datasets. By integrating PU learning, we enhance the 3D U-Net’s performance, demonstrating significant gains over traditional methods. This work pioneers the application of PU learning in Ca 2+ imaging analysis, offering a robust framework for mSCT detection and segmentation. We also demonstrate how this quantitative analysis pipeline can be used for subsequent mSCTs feature analysis. We characterize morphological and kinetic changes of mSCTs associated with the application of chemical long-term potentiation (cLTP) stimulation in cultured rat hippocampal neurons. Our data-driven approach shows that a cLTP-inducing stimulus leads to the emergence of new active dendritic regions and differently affects mSCTs subtypes.

2024-07-07

bioRxiv (preprint)

Predicting the Population Risk of Suicide Using Routinely Collected Health Administrative Data in Quebec, Canada: Model-Based Synthetic Estimation Study

JianLi Wang

Fatemeh Gholi Zadeh Kharrat

Geneviève Gariépy

Jean-François Pelletier

Victoria Massamba

Pascale Lévesque

Mada Mohammed

Alain Lesage

2024-06-27

JMIR Public Health and Surveillance (published)

Fatemeh Nourilenjan Nokabadi

Reproducibility Study on Adversarial Attacks Against Robust Transformer Trackers

Jean-François Lalonde

New transformer networks have been integrated into object tracking pipelines and have demonstrated strong performance on the latest benchmar… (see more)ks. This paper focuses on understanding how transformer trackers behave under adversarial attacks and how different attacks perform on tracking datasets as their parameters change. We conducted a series of experiments to evaluate the effectiveness of existing adversarial attacks on object trackers with transformer and non-transformer backbones. We experimented on 7 different trackers, including 3 that are transformer-based, and 4 which leverage other architectures. These trackers are tested against 4 recent attack methods to assess their performance and robustness on VOT2022ST, UAV123 and GOT10k datasets. Our empirical study focuses on evaluating adversarial robustness of object trackers based on bounding box versus binary mask predictions, and attack methods at different levels of perturbations. Interestingly, our study found that altering the perturbation level may not significantly affect the overall object tracking results after the attack. Similarly, the sparsity and imperceptibility of the attack perturbations may remain stable against perturbation level shifts. By applying a specific attack on all transformer trackers, we show that new transformer trackers having a stronger cross-attention modeling achieve a greater adversarial robustness on tracking datasets, such as VOT2022ST and GOT10k. Our results also indicate the necessity for new attack methods to effectively tackle the latest types of transformer trackers. The codes necessary to reproduce this study are available at https://github.com/fatemehN/ReproducibilityStudy.

2024-05-25

TMLR (accepted)

openreview.net

Explainable artificial intelligence models for predicting risk of suicide using health administrative data in Quebec

Fatemeh Gholi Zadeh Kharrat

Alain Lesage

Geneviève Gariépy

Jean-François Pelletier

Camille Brousseau-Paradis

Louis Rochette

Eric Pelletier

Pascale Lévesque

Mada Mohammed

JianLi Wang

Suicide is a complex, multidimensional event, and a significant challenge for prevention globally. Artificial intelligence (AI) and machine … (see more)learning (ML) have emerged to harness large-scale datasets to enhance risk detection. In order to trust and act upon the predictions made with ML, more intuitive user interfaces must be validated. Thus, Interpretable AI is one of the crucial directions which could allow policy and decision makers to make reasonable and data-driven decisions that can ultimately lead to better mental health services planning and suicide prevention. This research aimed to develop sex-specific ML models for predicting the population risk of suicide and to interpret the models. Data were from the Quebec Integrated Chronic Disease Surveillance System (QICDSS), covering up to 98% of the population in the province of Quebec and containing data for over 20,000 suicides between 2002 and 2019. We employed a case-control study design. Individuals were considered cases if they were aged 15+ and had died from suicide between January 1st, 2002, and December 31st, 2019 (n = 18339). Controls were a random sample of 1% of the Quebec population aged 15+ of each year, who were alive on December 31st of each year, from 2002 to 2019 (n = 1,307,370). We included 103 features, including individual, programmatic, systemic, and community factors, measured up to five years prior to the suicide events. We trained and then validated the sex-specific predictive risk model using supervised ML algorithms, including Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Multilayer perceptron (MLP). We computed operating characteristics, including sensitivity, specificity, and Positive Predictive Value (PPV). We then generated receiver operating characteristic (ROC) curves to predict suicides and calibration measures. For interpretability, Shapley Additive Explanations (SHAP) was used with the global explanation to determine how much the input features contribute to the models’ output and the largest absolute coefficients. The best sensitivity was 0.38 with logistic regression for males and 0.47 with MLP for females; the XGBoost Classifier with 0.25 for males and 0.19 for females had the best precision (PPV). This study demonstrated the useful potential of explainable AI models as tools for decision-making and population-level suicide prevention actions. The ML models included individual, programmatic, systemic, and community levels variables available routinely to decision makers and planners in a public managed care system. Caution shall be exercised in the interpretation of variables associated in a predictive model since they are not causal, and other designs are required to establish the value of individual treatments. The next steps are to produce an intuitive user interface for decision makers, planners and other stakeholders like clinicians or representatives of families and people with live experience of suicidal behaviors or death by suicide. For example, how variations in the quality of local area primary care programs for depression or substance use disorders or increased in regional mental health and addiction budgets would lower suicide rates.

2024-04-02

PLOS ONE (published)

Generalizing across Temporal Domains with Koopman Operators

Qiuhao Zeng

Wei Wang

Fan Zhou

Gezheng Xu

Ruizhi Pu

Changjian Shui

Shichun Yang

Boyu Wang

Charles Ling

2024-03-23

Proceedings of the AAAI Conference on Artificial Intelligence (published)

Analyzing Data Augmentation for Medical Images: A Case Study in Ultrasound Images

Adam Tupper

Data augmentation is one of the most effective techniques to improve the generalization performance of deep neural networks. Yet, despite of… (see more)ten facing limited data availability in medical image analysis, it is frequently underutilized. This appears to be due to a gap in our collective understanding of the efficacy of different augmentation techniques across medical imaging tasks and modalities. One domain where this is especially true is breast ultrasound images. This work addresses this issue by analyzing the effectiveness of different augmentation techniques for the classification of breast lesions in ultrasound images. We assess the generalizability of our findings across several datasets, demonstrate that certain augmentations are far more effective than others, and show that their usage leads to significant performance gains.

2024-03-13

ArXiv (preprint)

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

2023-12-07

ArXiv (preprint)

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

Jiaqi Li

Rui Wang

Yuanhao Lai

Changjian Shui

Sabyasachi Sahoo

Charles X. 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… (see more)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.

2023-11-25

ArXiv (preprint)

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 … (see more)novel Rényi-

2023-09-30

IEEE Transactions on Knowledge and Data Engineering (published)

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

2023-07-26

Nature Machine Intelligence (published)