Publications

Layerwise Early Stopping for Test Time Adaptation

Sabyasachi Sahoo

Mostafa ElAraby

Jonas Ngnawe

Yann Pequignot

Frederic Precioso

Christian Gagné

2024-04-04

ArXiv (prépublication)

doi.org

arxiv.org

Dynamic Neural Control Flow Execution: An Agent-Based Deep Equilibrium Approach for Binary Vulnerability Detection

Litao Li

Steven H. H. Ding

Andrew Walenstein

Philippe Charland

Benjamin Fung

2024-04-03

ArXiv (prépublication)

doi.org

arxiv.org

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

Fatemeh Gholi Zadeh Kharrat

Christian Gagné

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 … (voir plus)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.

2024-04-03

PLOS ONE (publié)

doi.org

Constrained Robotic Navigation on Preferred Terrains Using LLMs and Speech Instruction: Exploiting the Power of Adverbs

Faraz Lotfi

Farnoosh Faraji

Nikhil Kakodkar

Travis Manderson

David Meger

Gregory Dudek

2024-04-02

ArXiv (prépublication)

doi.org

arxiv.org

Is Meta-training Really Necessary for Molecular Few-Shot Learning ?

Philippe Formont

Hugo Jeannin

Pablo Piantanida

Ismail Ben Ayed

Few-shot learning has recently attracted significant interest in drug discovery, with a recent, fast-growing literature mostly involving con… (voir plus)voluted meta-learning strategies. We revisit the more straightforward fine-tuning approach for molecular data, and propose a regularized quadratic-probe loss based on the the Mahalanobis distance. We design a dedicated block-coordinate descent optimizer, which avoid the degenerate solutions of our loss. Interestingly, our simple fine-tuning approach achieves highly competitive performances in comparison to state-of-the-art methods, while being applicable to black-box settings and removing the need for specific episodic pre-training strategies. Furthermore, we introduce a new benchmark to assess the robustness of the competing methods to domain shifts. In this setting, our fine-tuning baseline obtains consistently better results than meta-learning methods.

2024-04-02

ArXiv (prépublication)

doi.org

arxiv.org

Acheiving United Nations' SDG3 Through Empowering Health Artificial Intelligence on Resource-Constrained Mobile Devices Without Connectivity

Tianyi Yang

Tianze Yang

Shaoshan Liu

Xue (Steve) Liu

At least half of the world's population do not have access to essential health services. Worse, large numbers of households are being pushed… (voir plus) into poverty because they must pay for health care out of their own pockets.

2024-04-01

SIGCAS Comput. Soc. (publié)

doi.org

Advanced MRI metrics improve the prediction of baseline disease severity for individuals with degenerative cervical myelopathy

Abdul Al-Shawwa

Kalum Ost

David Anderson

Newton Cho

Nathan Evaniew

W. Bradley Jacobs

Allan R. Martin

Ranjeet Gaekwad

Saswati Tripathy

Jacques Bouchard

Steven Casha

Roger Cho

Stephen duPlessis

Peter Lewkonia

Fred Nicholls

Paul T. Salo

Alex Soroceanu

Ganesh Swamy

Kenneth C. Thomas

Michael M.H. Yang … (voir 2 de plus)

Julien Cohen-Adad

David W. Cadotte

2024-04-01

The spine journal (publié)

doi.org

Co-developing longitudinal patient registries for phenylketonuria and mucopolysaccharidoses in Canada

John Adams

Kim Angel

John J. Mitchell

Pranesh Chakraborty

Beth K. Potter

Michal Inbar-Feigenberg

Sylvia Stockler

Monica Lamoureux

Alison H. Howie

Alex Pace

Nancy J. Butcher

Cheryl Rockman-Greenberg

Robin Hayeems

Anne-Marie Laberge

Thierry Lacaze-Masmonteil

Jeff Round

Martin Offringa

Maryam Oksoui

Andreas Schulze

Kathy N. Speechley … (voir 3 de plus)

Kednapa Thavorn

Yannis Trakadis

Kumanan Wilson

2024-04-01

Molecular Genetics and Metabolism (publié)

doi.org

Increasing schedule reliability in the multiple depot vehicle scheduling problem with stochastic travel time

L'ea Ricard

Guy Desaulniers

Andrea Lodi

Louis-Martin Rousseau

2024-04-01

Omega (publié)

doi.org

Machine Learning Robustness: A Primer

Houssem Ben Braiek

Foutse Khomh

This chapter explores the foundational concept of robustness in Machine Learning (ML) and its integral role in establishing trustworthiness … (voir plus)in Artificial Intelligence (AI) systems. The discussion begins with a detailed definition of robustness, portraying it as the ability of ML models to maintain stable performance across varied and unexpected environmental conditions. ML robustness is dissected through several lenses: its complementarity with generalizability; its status as a requirement for trustworthy AI; its adversarial vs non-adversarial aspects; its quantitative metrics; and its indicators such as reproducibility and explainability. The chapter delves into the factors that impede robustness, such as data bias, model complexity, and the pitfalls of underspecified ML pipelines. It surveys key techniques for robustness assessment from a broad perspective, including adversarial attacks, encompassing both digital and physical realms. It covers non-adversarial data shifts and nuances of Deep Learning (DL) software testing methodologies. The discussion progresses to explore amelioration strategies for bolstering robustness, starting with data-centric approaches like debiasing and augmentation. Further examination includes a variety of model-centric methods such as transfer learning, adversarial training, and randomized smoothing. Lastly, post-training methods are discussed, including ensemble techniques, pruning, and model repairs, emerging as cost-effective strategies to make models more resilient against the unpredictable. This chapter underscores the ongoing challenges and limitations in estimating and achieving ML robustness by existing approaches. It offers insights and directions for future research on this crucial concept, as a prerequisite for trustworthy AI systems.

2024-04-01

ArXiv (prépublication)

doi.org

arxiv.org

Self-supervised anomaly detection in computer vision and beyond: A survey and outlook.

Hadi Hojjati

Thi Kieu Khanh Ho

Narges Armanfard

2024-04-01

Neural Networks (publié)

doi.org

arxiv.org