Publications

Applying Recurrent Neural Networks and Blocked Cross-Validation to Model Conventional Drinking Water Treatment Processes
Aleksandar Jakovljevic
Laurent Charlin
Benoit Barbeau
The jar test is the current standard method for predicting the performance of a conventional drinking water treatment (DWT) process and opti… (see more)mizing the coagulant dose. This test is time-consuming and requires human intervention, meaning it is infeasible for making continuous process predictions. As a potential alternative, we developed a machine learning (ML) model from historical DWT plant data that can operate continuously using real-time sensor data without human intervention for predicting clarified water turbidity 15 min in advance. We evaluated three types of models: multilayer perceptron (MLP), the long short-term memory (LSTM) recurrent neural network (RNN), and the gated recurrent unit (GRU) RNN. We also employed two training methodologies: the commonly used holdout method and the theoretically correct blocked cross-validation (BCV) method. We found that the RNN with GRU was the best model type overall and achieved a mean absolute error on an independent production set of as low as 0.044 NTU. We further found that models trained using BCV typically achieve errors equal to or lower than their counterparts trained using holdout. These results suggest that RNNs trained using BCV are superior for the development of ML models for DWT processes compared to those reported in earlier literature.
2024-04-04
Water (published)
doi.org
Assessing the emergence time of SARS-CoV-2 zoonotic spillover
Stéphane Samson
Étienne Lord
Vladimir Makarenkov
2024-04-04
PLoS ONE (published)
doi.org
Layerwise Early Stopping for Test Time Adaptation
Sabyasachi Sahoo
Mostafa ElAraby
Jonas Ngnawe
Yann Pequignot
Frederic Precioso
Christian Gagné
2024-04-04
ArXiv (preprint)
arxiv.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 (preprint)
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 … (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.
2024-04-03
PLOS ONE (published)
doi.org
Rethinking Teacher-Student Curriculum Learning through the Cooperative Mechanics of Experience
Manfred Diaz
Liam Paull
Andrea Tacchetti
Teacher-Student Curriculum Learning (TSCL) is a curriculum learning framework that draws inspiration from human cultural transmission and le… (see more)arning. It involves a teacher algorithm shaping the learning process of a learner algorithm by exposing it to controlled experiences. Despite its success, understanding the conditions under which TSCL is effective remains challenging. In this paper, we propose a data-centric perspective to analyze the underlying mechanics of the teacher-student interactions in TSCL. We leverage cooperative game theory to describe how the composition of the set of experiences presented by the teacher to the learner, as well as their order, influences the performance of the curriculum that is found by TSCL approaches. To do so, we demonstrate that for every TSCL problem, there exists an equivalent cooperative game, and several key components of the TSCL framework can be reinterpreted using game-theoretic principles. Through experiments covering supervised learning, reinforcement learning, and classical games, we estimate the cooperative values of experiences and use value-proportional curriculum mechanisms to construct curricula, even in cases where TSCL struggles. The framework and experimental setup we present in this work represent a novel foundation for a deeper exploration of TSCL, shedding light on its underlying mechanisms and providing insights into its broader applicability in machine learning.
2024-04-03
ArXiv (preprint)
doi.org
arxiv.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 (preprint)
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… (see more)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 (preprint)
doi.org
arxiv.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 … (see 2 more)
Julien Cohen-Adad
David W. Cadotte
2024-04-01
The spine journal (published)
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 … (see 3 more)
Kednapa Thavorn
Yannis Trakadis
Kumanan Wilson
2024-04-01
Molecular Genetics and Metabolism (published)
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 (published)
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 … (see more)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 (preprint)
doi.org
arxiv.org