Publications

Using an eye tracker to capture reading skills as measured by a digital adaptation of TOWRE-2
Krystle-Lee Turgeon
Zero-Shot NAS for TinyML Semantic Segmentation via Weight Sharing
Zhuoran Xiong
Warren J. Gross
Brett Meyer
On the geometry and topology of representations: the manifolds of modular addition
The Clock and Pizza interpretations, associated with architectures differing in either uniform or learnable attention, were introduced to ar… (voir plus)gue that different architectural designs can yield distinct circuits for modular addition. In this work, we show that this is not the case, and that both uniform attention and trainable attention architectures implement the same algorithm via topologically and geometrically equivalent representations. Our methodology goes beyond the interpretation of individual neurons and weights. Instead, we identify all of the neurons corresponding to each learned representation and then study the collective group of neurons as one entity. This method reveals that each learned representation is a manifold that we can study utilizing tools from topology. Based on this insight, we can statistically analyze the learned representations across hundreds of circuits to demonstrate the similarity between learned modular addition circuits that arise naturally from common deep learning paradigms.
Combining Constraint Programming and Machine Learning: From Current Progress to Future Opportunities
Tias Guns
Michele Lombardi
Gilles Pesant
Dimos Tsouros
The integration of constraint programming (CP) together with machine learning (ML) has emerged as a promising direction for tackling complex… (voir plus) decision-making and combinatorial optimization problems. While CP offers expressive modeling capabilities and formal guarantees, ML provides adaptive methods for learning from data and generalizing across instances. This survey presents a comprehensive overview of recent advances in combining CP and ML. We first show how ML has been used to improve the CP toolbox, both in modeling and in the efficiency of solving. Then, we examine how CP can support ML, particularly in providing structure, guarantees, and symbolic reasoning capabilities. Finally, we identify key open challenges inherent to such hybrid approaches and outline promising directions for future research. This survey provides a first conceptual and structured review of recent advancements in this emerging field, aiming to serve as a resource for practitioners and researchers in both the CP and ML communities. To keep the progress up to date, a curated list of references is hosted on an accompanying repository (https://github.com/corail-research/CPML-paper-list) and is open to community contributions.
Family caregivers' acceptance of Artificial Intelligence-enabled technologies for providing care to older adults
Amanda Yee
Mark J. Yaffe
Tibor Schuster
Sylvie Lambert
Artificial intelligence (AI)-enabled technologies hold promise for assisting in the care of an aging population. Few studies have focused on… (voir plus) exploring family caregivers’ (FCGs) behavioural intention of using such innovation, and even fewer have employed a technology acceptance framework. This study examined FCGs of older adults’ behavioural intention of using AI-enabled technologies for caregiving. We conducted a theory-based cross-sectional quantitative survey. Eligible FCGs for this study were: (1) aged 45–64; (2) residing in Quebec, Canada; (3) providing care for at least one older adult (65+); (4) having access to a computer or smartphone with internet connectivity; and, (5) having proficiency in reading and comprehending English or French. We adapted and expanded the Unified Theory of Acceptance and Use of Technology (UTAUT) framework to measure their behavioural intention of using AI-enabled technologies for caregiving. We used descriptive statistics and a random forest model to assess the most important predictive factors across nine variables and their direction of association with behavioural intention. The Consensus-Based Checklist for Reporting of Survey Studies (CROSS) guidelines was used for reporting the study’s results. Among the polling firm’s 100,000 panelists, 2740 eligible individuals were randomly chosen to receive an email invitation to the study. Of 465 panelists who opened the survey (i.e., unique visitors),199 were eligible and completed the online survey. The random forest model explained between 56% and 86% of the behavioural intention variance of using AI, with social influence demonstrating the highest predictive relevance as indicated by a 35% increase in mean-squared error once removed from the model. Among the nine variables considered, six demonstrated a positive association with behavioural intention. These variables included social influence, effort expectancy, performance expectancy, perceived trust, confidence in healthcare professionals’ advice for the use of AI-enabled technologies, and facilitating connditions. The variables perceived cost and technology anxiety indicated a negative association with behavioural intention. Our extended UTAUT model identified factors associated with FCGs' intention to use AI. While all nine variables contributed, attitudes toward AI within caregivers’ social circles was the strongest predictor. Stakeholders from industry, government, and healthcare can enhance the adoption of AI-enabled technologies in older adult care by leveraging facilitators and addressing barriers experienced by caregivers.
MS-SSM: A Multi-Scale State Space Model for Efficient Sequence Modeling
Mahdi Karami
Ali Behrouz
Peilin Zhong
Seyed Vahab Mirrokni
State-space models (SSMs) have recently attention as an efficient alternative to computationally expensive attention-based models for sequen… (voir plus)ce modeling. They rely on linear recurrences to integrate information over time, enabling fast inference, parallelizable training, and control over recurrence stability. However, traditional SSMs often suffer from limited effective memory, requiring larger state sizes for improved recall. Moreover, existing SSMs struggle to capture multi-scale dependencies, which are essential for modeling complex structures in time series, images, and natural language. This paper introduces a multi-scale SSM framework that addresses these limitations by representing sequence dynamics across multiple resolution and processing each resolution with specialized state-space dynamics. By capturing both fine-grained, high-frequency patterns and coarse, global trends, MS-SSM enhances memory efficiency and long-range modeling. We further introduce an input-dependent scale-mixer, enabling dynamic information fusion across resolutions. The proposed approach significantly improves sequence modeling, particularly in long-range and hierarchical tasks, while maintaining computational efficiency. Extensive experiments on benchmarks, including Long Range Arena, hierarchical reasoning, time series classification, and image recognition, demonstrate that MS-SSM consistently outperforms prior SSM-based models, highlighting the benefits of multi-resolution processing in state-space architectures.
Multi-Agent Framework for Threat Mitigation and Resilience in AI-Based Systems
Armstrong Foundjem
Lionel Nganyewou Tidjon
Leuson Da Silva
Probabilistic Modelling is Sufficient for Causal Inference
Bruno Mlodozeniec
David S. Krueger
Richard E. Turner
Causal inference is a key research area in machine learning, yet confusion reigns over the tools needed to tackle it. There are prevalent cl… (voir plus)aims in the machine learning literature that you need a bespoke causal framework or notation to answer causal questions. In this paper, we want to make it clear that you \emph{can} answer any causal inference question within the realm of probabilistic modelling and inference, without causal-specific tools or notation. Through concrete examples, we demonstrate how causal questions can be tackled by writing down the probability of everything. Lastly, we reinterpret causal tools as emerging from standard probabilistic modelling and inference, elucidating their necessity and utility.
Enhanced Multi-Class Arrhythmia Detection Using Generative Adversarial Networks for Minority Class Augmentation
Heba Ismail
Mohamed Adel Serhani
Benjamin C. M. Fung
Foundation models for electrocardiogram interpretation: clinical implications
Achille Sowa
Jacques Delfrate
Olivier Tastet
Denis Corbin
Merve Kulbay
Derman Ozdemir
Marie-Jeanne Noël
François-Christophe Marois-Blanchet
François Harvey
Surbhi Sharma
Minhaj Ansari
I-Min Chiu
Valentina D'souza
Sam F. Friedman
Michael Chassé
Brian J. Potter
Jonathan Afilalo
Pierre Adil Elias
Gilbert Jabbour … (voir 13 de plus)
Mourad Bahani
Marie-Pierre Dubé
Patrick M. Boyle
Neal A. Chatterjee
Joshua Barrios
Geoffrey H. Tison
David Ouyang
Mahnaz Maddah
Shaan Khurshid
Julia Cadrin-Tourigny
Rafik Tadros
Robert Avram
The 12-lead electrocardiogram (ECG) remains a cornerstone of cardiac diagnostics, yet existing artificial intelligence (AI) solutions for au… (voir plus)tomated interpretation often lack generalizability, remain closed source, and are primarily trained using supervised learning (SL), which requires extensive labelled datasets and may limit adaptability across diverse clinical settings. Self-supervised learning (SSL) can potentially overcome these limitations by learning robust representations from unlabelled data. To address these challenges, this study developed and compared two open-source foundational ECG models: DeepECG-SL, a supervised multilabel ECG model, and DeepECG-SSL, a self-supervised model. Both models were trained on over 1 million ECGs using a standardized preprocessing pipeline and automated free-text extraction from ECG reports to predict 77 cardiac conditions. DeepECG-SSL leveraged unlabelled data through self-supervised contrastive learning and masked lead modelling before fine-tuning for downstream tasks, while DeepECG-SL was trained directly on labelled diagnostic data in an end-to-end fashion. Performance was evaluated across seven private, multilingual healthcare systems and four public ECG repositories, with assessment of fairness by age and sex, and investigation of privacy vulnerabilities as well as memory and compute requirements. DeepECG-SSL achieved micro-averaged area under the receiver operating characteristic curves (AUROCs) across all 77 cardiac conditions for ECG interpretation of 0.990 [95% confidence interval (CI): 0.990, 0.990] on the internal dataset (MHI-ds), 0.981 (95% CI: 0.981, 0.981) on external public datasets (UKB, CLSA, MIMIC-IV and PTB), and 0.983 (95% CI: 0.983, 0.983) on external private datasets (UW, UCSF, JGH, NYP, MGH, CSH and CHUM), while DeepECG-SL demonstrated AUROCs of 0.992 (95% CI: 0.992, 0.992), 0.980 (95% CI: 0.980, 0.980), and 0.983 (95% CI: 0.983, 0.984), respectively. Fairness analyses revealed minimal disparities (true-positive rate and false-positive rate difference <0.1) across age and sex groups for both models. DeepECG-SSL demonstrated superior performance on limited-data digital biomarker tasks, with the largest improvements in long QT syndrome (LQTS) genotype classification (AUROC 0.931 vs 0.850, P = .026, n = 127 ECGs) and 5 year atrial fibrillation risk prediction (AUROC 0.742 vs 0.734, P < 0.001, n = 132 050 ECGs), while achieving superior performance in left ventricular ejection fraction ≤40% classification (AUROC 0.926 vs 0.917, P < 0.001, n = 25 252 ECGs) and comparable performance in LQTS detection (AUROC 0.767 vs 0.735, P = 0.117, n = 934 ECGs). This study establishes SSL as a promising paradigm for ECG analysis, particularly in settings with limited annotated data, enhancing accessibility, generalizability, and fairness in AI-driven cardiac diagnostics. By releasing model weights, preprocessing tools, and validation code, this work aims to support robust, data-efficient AI diagnostics across diverse clinical environments and questions.
Now is the time: operationalizing generative neurophenomenology through interpersonal methods
Anne Monnier
Lena Adel

Lived experience is shaped by intersubjective, social, cultural, and historical dimensions. For the past 30 years, neurophenomenology has… (voir plus) adopted an embodied perspective of the mind by integrating first-person experiential and third-person neurobehavioral perspectives. Indeed, the neurophenomenology pragmatic approach has embraced an embodied perspective of the mind by integrating experiential first-person and neurobehavioural third-person perspectives. Neurophenomenology reveals mutual constraints between both, as they co-constitute a person’s lived experience. This article emphasizes the intersubjective and social facets of lived experience as well as the readiness of the scientific community to use a "generative neurophenomenology" approach, envisioned in the 1990s by Francisco Varela. For this endeavour, we clarify three meanings of “generative” as it applies distinctly to generative phenomenology, generative passages, and generative models. Then, we propose to combine existing methods to update neurophenomenology program: First, by transitioning from individual to multiple people phenomenology methods that include intersubjectivity experience; second, by expanding traditional neuroscience to include measures of multimodal interpersonal synchrony; and third, by leveraging multiple computational tools to integrate different viewpoints, thereby enriching our understanding of lived experience; We also underscore the potential of diverse mathematical formalisms to capture aspects of human experience, all while underscoring that using computational approaches to model neurophenomenology does not entail endorsing computationalism as a grounding hypothesis of human experience. Finally, we illustrate the clinical relevance of this paradigm through two case studies in psychiatry—(1) with interactive dyads in autism and (2) with multiple members in family therapy sessions—demonstrating its translational potential.

Causally informed, multifactorial pathways linking cognition and personality to adolescent mental health
Jiadong Yan
Bin Wan
Paule Joanne Toussaint
Judy Chen
Gleb Bezgin
Yasser Iturria-Medina
Alan Evans
Sherif Karama
Adolescence is a sensitive period for the emergence of psychopathology. During this time, physiological changes and environmental exposures … (voir plus)jointly shape brain development and influence cognitive and personality maturation, collectively heightening vulnerability to mental disorders. However, the complexity of interactions between these factors has hindered a systems-level understanding of mental health and the causal roles of cognition and personality in psychopathology. In this study, we proposed a multifactorial causal framework integrating brain, pubertal, environmental, and behavioral factors to characterize heterogeneity in adolescent mental health trajectories at the individual level. We then investigated latent causal pathways linking cognition and personality to mental health outcomes and identified potential personalized intervention targets. Leveraging the Adolescent Brain Cognitive Development (ABCD) dataset ( N = 4,501), we analyzed 165 behavioral pairs connecting cognition and personality traits to mental health symptoms. Using cross-sectional multivariate mediation and longitudinal interaction-inclusive analyses, we identified 68 behavioral pairs showing significant causal relationships, with brain and environmental exposures contributing to most pathways, while pubertal factors exhibited limited involvement. Individualized interpretive analyses further revealed 23 pairs suggesting potential interventions with response rates exceeding 50%. Among these, behavioral inhibition, negative urgency, and processing speed emerged as the most common intervention targets, whereas psychosis symptoms and attention problems were the most likely issues to improve. Overall, our study advances a comprehensive framework capturing the multifactorial and heterogeneous nature of adolescent mental health, delineates specific causal pathways from cognitive and personality traits to psychopathology, and provides a principled basis for potential individualized intervention strategies.