Publications

Swarm robotics localization: comparing methods from infrared to foundation models
Ali Imran
Vivek Shankar Vardharajan
Rafael Gomes Braga
Giovanni Beltrame
David St-Onge
2026-02-01
Swarm Intelligence (publié)
doi.org
Vector Quantized Latent Concepts: A Scalable Alternative to Clustering-Based Concept Discovery
Xuemin Yu
Ankur Garg
S Ebrahimi Kahou
Hassan Sajjad
Deep Learning models encode rich semantic information in their hidden representations. However, it remains challenging to understand which p… (voir plus)arts of this information models actually rely on when making predictions. A promising line of post-hoc concept-based explanation methods relies on clustering token representations. However, commonly used approaches such as hierarchical clustering are computationally infeasible for large-scale datasets, and K-Means often yields shallow or frequency-dominated clusters. We propose the vector quantized latent concept (VQLC) method, a framework built upon the vector quantized-variational autoencoder (VQ-VAE) architecture that learns a discrete codebook mapping continuous representations to concept vectors. We perform thorough evaluations and show that VQLC improves scalability while maintaining comparable quality of human-understandable explanations.
2026-02-01
ArXiv (prépublication)
arxiv.org
arxiv.org
Adapting Language Models to Produce Good Class Probabilities for Classification Tasks
Lautaro Estienne
Matias Vera
Elizabeth Fons
Elena Kochkina
Pablo Piantanida
LUCIANA FERRER
Large generative language models (GLM) provide a versatile tool for solving a wide variety of natural processing tasks. GLM responses, thoug… (voir plus)h, are provided in the form of text, without an indication of the model's confidence in the answer. This limits the usability of these models on high-risk applications where decisions made based on an incorrect answer can have severe consequences. In this work, we focus on the problem of generating class posterior distributions for text classification tasks like sentiment, news category and intent classification. These posteriors can be used for decision making and as interpretable scores for the user. We show that the naive approach for computing posteriors based on the token posteriors produced by the GLM results in extremely poor posteriors. We then explore different adaptation approaches for improving the quality of posteriors, focusing on low resource scenarios where a small amount of data is available for adaptation. We show that parameter-efficient supervised fine-tuning (SFT), while providing large gains in terms of decision quality, produces suboptimal posteriors due to overfitting. To address this problem, we propose an approach that combines SFT and post-hoc calibration (PHC) using a three-stage training strategy, improving the quality of both posteriors and categorical decisions.
2026-01-31
Transactions on Machine Learning Research (accepté)
openreview.net
openreview.net
Assessing Language Bias in Pediatric Surgical Systematic Reviews: A Meta-epidemiological Study.
Dunya Moghul
Elena Guadagno
Shreenik Kundu
Dan Poenaru
Robert Baird
2026-01-31
Journal of Pediatric Surgery (publié)
doi.org
ASSESSMENT OF PREGNANT WOMEN'S INTENTION TO USE A MOBILE APPLICATION-BASED DECISION AID FOR PRENATAL SCREENING FOR TRISOMIES 21, 18 AND 13: A MIXED-METHODS CROSS-SECTIONAL STUDY
Candide Ahouehome
Alexandre Bureau
S. A. Rahimi
S. Gadio
Yan Julien
O. Assan
S. Guay-Bélanger
François Rousseau
Jean-Claude Forest
Sylvie Langlois
Vardit Ravitsky
Patrick Archambault
F. Légaré
2026-01-31
Patient Education and Counseling (publié)
doi.org
Efficient Self-Supervised Barlow Twins from Limited Tissue Slide Cohorts for Colonic Pathology Diagnostics
Cassandre Notton
Vasudev Sharma
Vincent Quoc-Huy Trinh
Lina Chen
Minqi Xu
Sonal Varma
Mahdi S. Hosseini
Colorectal cancer (CRC) is one of the few cancers that have an established dysplasia-carcinoma sequence that benefits from screening. Everyo… (voir plus)ne over 50 years of age in Canada is eligible for CRC screening. About 20\% of those people will undergo a biopsy for a pre-neoplastic polyp and, in many cases, multiple polyps. As such, these polyp biopsies make up the bulk of a pathologist's workload. Developing an efficient computational model to help screen these polyp biopsies can improve the pathologist's workflow and help guide their attention to critical areas on the slide. DL models face significant challenges in computational pathology (CPath) because of the gigapixel image size of whole-slide images and the scarcity of detailed annotated datasets. It is, therefore, crucial to leverage self-supervised learning (SSL) methods to alleviate the burden and cost of data annotation. However, current research lacks methods to apply SSL frameworks to analyze pathology data effectively. This paper aims to propose an optimized Barlow Twins framework for colorectal polyps screening. We adapt its hyperparameters, augmentation strategy and encoder to the specificity of the pathology data to enhance performance. Additionally, we investigate the best Field of View (FoV) for colorectal polyps screening and propose a new benchmark dataset for CRC screening, made of four types of colorectal polyps and normal tissue, by performing downstream tasking on MHIST and NCT-CRC-7K datasets. Furthermore, we show that the SSL representations are more meaningful and qualitative than the supervised ones and that Barlow Twins benefits from the Swin Transformer when applied to pathology data. Codes are avaialble from https://github.com/AtlasAnalyticsLab/PathBT.
2026-01-31
Medical Image Analysis (publié)
doi.org
arxiv.org
Forest-Guided Semantic Transport for Label-Supervised Manifold Alignment
Adrien Aumon
Myriam Lizotte
Guy Wolf
Kevin R. Moon
Jake S. Rhodes
2026-01-31
ArXiv (prépublication)
arxiv.org
arxiv.org
Morphometric dissimilarity in association cortices linked to autism subtype with more severe symptoms
Hongxiu Jiang
Raul Rodriguez-Cruces
Ke Xie
Valeria Kebets
Yezhou Wang
Clara F. Weber
Ying He
Jonah Kember
Hilary Sweatman
Zeus Gracia Tabuenca
Jean-Baptiste Poline
Danilo Bzdok
Seok-Jun Hong
Boris Bernhardt
Xiaoqian Chai
Autism spectrum disorder (ASD) is a prevalent and heterogeneous neurodevelopmental condition marked by atypical brain connectivity. Understa… (voir plus)nding ASD neural subtypes at the network level is critical for clarifying its neuroanatomical heterogeneity. Morphometric similarity networks (MSNs), derived from region-to-region similarity across multiple anatomical features, offer a powerful approach for capturing individual-level neural architecture. In this study, MSNs were estimated from seven anatomical features in 348 individuals with ASD and 452 typically developing (TD) controls. Across all ASD participants, the first principal component of MSN values was negatively correlated with social and communication severity. Three ASD subtypes with distinct MSN patterns were identified. Subtype-1, characterized by weaker morphometric similarity values in frontotemporal association regions compared to TD individuals, exhibited the most severe symptoms in social, communication and repetitive behaviors, and displayed hyperconnectivity between the salience and visual networks, and between language and visual networks. Subtype-2 showed greater values of morphometric similarities than TD and less severe social symptoms compared to subtype-1, along with hyperconnectivity between default and salience networks relative to TD. Subtype-3 displayed morphometric similarity values largely comparable to TD and the least severe symptoms out of the three subtypes. Transcriptomic analysis revealed that GABAergic parvalbumin and glutamatergic intratelencephalic-projecting neurons were key cell types differentiating subtypes. These findings suggest the existence of distinct ASD neuroanatomical subtypes defined by regional morphometric similarity, each linked to unique behavioral, functional, and transcriptomic profiles. Morphometric dissimilarity in association regions may serve as a neural signature for ASD subtypes characterized by more severe clinical manifestations.
2026-01-31
NeuroImage (publié)
doi.org
Telomere-to-telomere assembly detects genomic diversity in Canadian strains of
<i>Borrelia burgdorferi</i>
Atia B. Amin
Ana Victoria Ibarra Meneses
Simon Gagnon
Georgi Merhi
Martin Olivier
Momar Ndao
Mathieu Blanchette
Christopher Fernandez-Prada
David Langlais
2026-01-31
Cell Reports (publié)
doi.org
Threading the needle: Practical considerations for merging theory-driven computational psychiatry with data-driven analytics to enhance precision health at scale
Annie Cheng
Anna Konova
Albert Powers
Philip Corlett
Ifat Levy
Xiaosi Gu
Quentin Huys
Helen Pushkarskya
Sarah Fineberg
Tobias Hauser
Danilo Bzdok
Ilan Harpaz-Rotem
Theresa Babuscio
Lisa Nichols
Yize Zhao
Manu Sharma
Daniella Meeker
Hua Xu
Robb B. Rutledge
Godfrey D. Pearlson … (voir 2 de plus)
Christopher Pittenger
Sarah W. Yip
The rapidly evolving field of computational psychiatry enables quantification of specific cognitive processes, and their underlying mechanis… (voir plus)ms, in a translational and potentially scalable manner, using a combination of data collection via mechanistically informed behavioral tasks and theory-driven mathematical modeling. In parallel, transdiagnostic, dimensional approaches to psychiatric diagnostics, such as RDoC and HiTOP, seek to facilitate links between clinical research and real-world clinical reality, which rarely respects traditional diagnostic boundaries. These two approaches are seldom combined. In addition, while most psychiatric disorders are defined by their longitudinal course, our ability to predict symptom trajectories and tailor treatments to the individual remains limited, in part due to a dearth of longitudinal data collected using assessments sensitive to individual change over time. To address these gaps, the recently launched 'Individually Measured Phenotypes to Advance Computational Translation at Yale' (IMPACT-Y) study is collecting longitudinal data from a transdiagnostic cohort of 2400 individuals, using a combination of 'traditional' clinical research methods (e.g., health records, standardized assessments) and more novel computational approaches (e.g., behavioral tasks with demonstrated sensitivity to latent constructs and to within-person change, spoken narrative data). Here, we discuss unique challenges and opportunities in study design and analysis considerations of IMPACT-Y. Incorporating both theory- and data-driven analytics, we hope that IMPACT-Y will provide an unprecedented resource for characterizing longitudinal trajectories of core computational psychiatry constructs (e.g., reward learning) within and between individuals, for parsing heterogeneity beyond traditional diagnostic categories, and for linking inter- and intra-individual clinical variability to underlying mechanisms.
2026-01-31
Biological Psychiatry: Cognitive Neuroscience and Neuroimaging (publié)
doi.org
VOCALoco: Viability-Optimized Cost-aware Adaptive Locomotion
Stanley Wu
Mohamad H. Danesh
Simon Li
Hanna Yurchyk
Amin Abyaneh
Anas El Houssaini
David Meger
Hsiu-Chin Lin
Recent advancements in legged robot locomotion have facilitated traversal over increasingly complex terrains. Despite this progress, many ex… (voir plus)isting approaches rely on end-to-end deep reinforcement learning (DRL), which poses limitations in terms of safety and interpretability, especially when generalizing to novel terrains. To overcome these challenges, we introduce VOCALoco, a modular skill-selection framework that dynamically adapts locomotion strategies based on perceptual input. Given a set of pre-trained locomotion policies, VOCALoco evaluates their viability and energy-consumption by predicting both the safety of execution and the anticipated cost of transport over a fixed planning horizon. This joint assessment enables the selection of policies that are both safe and energy-efficient, given the observed local terrain. We evaluate our approach on staircase locomotion tasks, demonstrating its performance in both simulated and real-world scenarios using a quadrupedal robot. Empirical results show that VOCALoco achieves improved robustness and safety during stair ascent and descent compared to a conventional end-to-end DRL policy
2026-01-31
IEEE Robotics and Automation Letters (publié)
doi.org
arxiv.org
Parallel Stochastic Gradient-Based Planning for World Models
Michael Psenka
Michael G. Rabbat
Aditi Krishnapriyan
Yann Lecun
Amir Bar
World models simulate environment dynamics from raw sensory inputs like video. However, using them for planning can be challenging due to th… (voir plus)e vast and unstructured search space. We propose a robust and highly parallelizable planner that leverages the differentiability of the learned world model for efficient optimization, solving long-horizon control tasks from visual input. Our method treats states as optimization variables ("virtual states") with soft dynamics constraints, enabling parallel computation and easier optimization. To facilitate exploration and avoid local optima, we introduce stochasticity into the states. To mitigate sensitive gradients through high-dimensional vision-based world models, we modify the gradient structure to descend towards valid plans while only requiring action-input gradients. Our planner, which we call GRASP (Gradient RelAxed Stochastic Planner), can be viewed as a stochastic version of a non-condensed or collocation-based optimal controller. We provide theoretical justification and experiments on video-based world models, where our resulting planner outperforms existing planning algorithms like the cross-entropy method (CEM) and vanilla gradient-based optimization (GD) on long-horizon experiments, both in success rate and time to convergence.
2026-01-30
ArXiv (prépublication)
arxiv.org
arxiv.org