Publications

Layerwise Early Stopping for Test Time Adaptation
Sabyasachi Sahoo
Mostafa ElAraby
Jonas Ngnawe
Yann Pequignot
Frederic Precioso
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
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 … (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.
Constrained Robotic Navigation on Preferred Terrains Using LLMs and Speech Instruction: Exploiting the Power of Adverbs
Faraz Lotfi
Farnoosh Faraji
Nikhil Kakodkar
Travis Manderson
Is Meta-training Really Necessary for Molecular Few-Shot Learning ?
Philippe Formont
Hugo Jeannin
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.
Acheiving United Nations' SDG3 Through Empowering Health Artificial Intelligence on Resource-Constrained Mobile Devices Without Connectivity
Tianyi Yang
Tianze Yang
Shaoshan 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.
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)
David W. Cadotte
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
Kumanan Wilson
Increasing schedule reliability in the multiple depot vehicle scheduling problem with stochastic travel time
L'ea Ricard
Guy Desaulniers
Louis-Martin Rousseau
Machine Learning Robustness: A Primer
Houssem Ben Braiek
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.
Self-supervised anomaly detection in computer vision and beyond: A survey and outlook.
Hadi Hojjati
Thi Kieu Khanh Ho
Scaling up ridge regression for brain encoding in a massive individual fMRI dataset
Sana Ahmadi
Tristan Glatard