Publications

Building spatial world models from sparse transitional episodic memories

Zizhan He

Maxime Daigle

Pouya Bashivan

Many animals possess a remarkable capacity to rapidly construct flexible mental models of their environments. These world models are crucial… (voir plus) for ethologically relevant behaviors such as navigation, exploration, and planning. The ability to form episodic memories and make inferences based on these sparse experiences is believed to underpin the efficiency and adaptability of these models in the brain. Here, we ask: Can a neural network learn to construct a spatial model of its surroundings from sparse and disjoint episodic memories? We formulate the problem in a simulated world and propose a novel framework, the Episodic Spatial World Model (ESWM), as a potential answer. We show that ESWM is highly sample-efficient, requiring minimal observations to construct a robust representation of the environment. It is also inherently adaptive, allowing for rapid updates when the environment changes. In addition, we demonstrate that ESWM readily enables near-optimal strategies for exploring novel environments and navigating between arbitrary points, all without the need for additional training.

2025-05-18

ArXiv (prépublication)

Generalizable Imitation Learning Through Pre-Trained Representations

Wei-Di Chang

Francois Hogan

Scott Fujimoto

Gregory Dudek

In this paper we leverage self-supervised vision transformer models and their emergent semantic abilities to improve the generalization abil… (voir plus)ities of imitation learning policies. We introduce BC-ViT, an imitation learning algorithm that leverages rich DINO pre-trained Visual Transformer (ViT) patch-level embeddings to obtain better generalization when learning through demonstrations. Our learner sees the world by clustering appearance features into semantic concepts, forming stable keypoints that generalize across a wide range of appearance variations and object types. We show that this representation enables generalized behaviour by evaluating imitation learning across a diverse dataset of object manipulation tasks. Our method, data and evaluation approach are made available to facilitate further study of generalization in Imitation Learners.

2025-05-18

2025 IEEE International Conference on Robotics and Automation (ICRA) (publié)

Half Search Space is All You Need

Pavel Rumiantsev

Mark J. Coates

2025-05-18

ArXiv (prépublication)

Learning active tactile perception through belief-space control

Jean-François Tremblay

Johanna Hansen

Francois Hogan

Gregory Dudek

Robot operating in an open world can encounter novel objects with unknown physical properties, such as mass, friction, or size. It is desira… (voir plus)ble to be able to sense those property through contact-rich interaction, before performing downstream tasks with the objects. We propose a method for autonomously learning active tactile perception policies, by learning a generative world model leveraging a differentiable bayesian filtering algorithm, and designing an information- gathering model predictive controller. We test the method on three simulated tasks: mass estimation, height estimation and toppling height estimation. Our method is able to discover policies which gather information about the desired property in an intuitive manner.

2025-05-18

2025 IEEE International Conference on Robotics and Automation (ICRA) (publié)

openreview.net

PedMedQA: Comparing Large Language Model Accuracy in Pediatric and Adult Medicine

Nikhil Jaiswal

Yuanchao Ma

Bertrand Lebouché

Dan Poenaru

Esli Osmanlliu

Large language models (LLMs) have the potential to revolutionize healthcare, including aiding in clinical decision-making. However, recent w… (voir plus)ork suggests that LLM performance in pediatric cases may be weaker than adult cases. A key limitation in evaluating these differences is the lack of pediatric-specific benchmarks, making it difficult to systematically assess how well LLMs generalize to pediatric scenarios.

2025-05-18

Pediatrics Open Science (publié)

RobusTAD: reference panel based annotation of nested topologically associating domains

Yanlin Zhang

Rola Dali

Mathieu Blanchette

Topologically associating domains (TADs) are fundamental units of 3D genomes and play essential roles in gene regulation. Hi-C data suggests… (voir plus) a hierarchical organization of TADs. Accurately annotating nested TADs from Hi-C data remains challenging, both in terms of the precise identification of boundaries and the correct inference of hierarchies. While domain boundary is relatively well conserved across cells, few approaches have taken advantage of this fact. Here, we present RobusTAD to annotate TAD hierarchies. It incorporates additional Hi-C data to refine boundaries annotated from the study sample. RobusTAD outperforms existing tools at boundary and domain annotation across several benchmarking tasks. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-025-03568-9.

2025-05-18

Genome Biology (publié)

Seeing the Unseen: How EMoE Unveils Bias in Text-to-Image Diffusion Models

Lucas Berry

Axel Brando

Wei-Di Chang

Juan Higuera

2025-05-18

ArXiv (prépublication)

openreview.net

Topological mapping for traversability-aware long-range navigation in off-road terrain

Jean-François Tremblay

Julie Alhosh

Louis Petit

Faraz Lotfi

Lara Landauro

Autonomous robots navigating in off-road terrain like forests open new opportunities for automation. While off-road navigation has been stud… (voir plus)ied, existing work often relies on clearly delineated pathways. We present a method allowing for long-range planning, exploration and low-level control in unknown off-trail forest terrain, using vision and GPS only. We represent outdoor terrain with a topological map, which is a set of panoramic snapshots connected with edges containing traversability information. A novel traversability analysis method is demonstrated, predicting the existence of a safe path towards a target in an image. Navigating between nodes is done using goal-conditioned behavior cloning, leveraging the power of a pretrained vision transformer. An exploration planner is presented, efficiently covering an unknown off-road area with unknown traversability using a frontiers-based approach. The approach is successfully deployed to autonomously explore two 400 m2 forest sites unseen during training, in difficult conditions for navigation.

2025-05-18

2025 IEEE International Conference on Robotics and Automation (ICRA) (publié)

FedWeight: mitigating covariate shift of federated learning on electronic health records data through patients re-weighting

He Zhu

Jun Bai

Na Li

Xiaoxiao Li

Dianbo Liu

David L. Buckeridge

Yue Li

Federated learning (FL) enables collaborative analysis of decentralized medical data while preserving patient privacy. However, the covariat… (voir plus)e shift from demographic and clinical differences can reduce model generalizability. We propose FedWeight, a novel FL framework that mitigates covariate shift by reweighting patient data from the source sites using density estimators, allowing the trained model to better align with the distribution of the target site. To support unsupervised applications, we introduce FedWeight ETM, a federated embedded topic model. We evaluated FedWeight in cross-site FL on the eICU dataset and cross-dataset FL between eICU and MIMIC III. FedWeight consistently outperforms standard FL baselines in predicting ICU mortality, ventilator use, sepsis diagnosis, and length of stay. SHAP-based interpretation and ETM-based topic modeling reveal improved identification of clinically relevant characteristics and disease topics associated with ICU readmission.

2025-05-16

NPJ Digital Medicine (publié)

Bidirectional Information Flow (BIF) -- A Sample Efficient Hierarchical Gaussian Process for Bayesian Optimization

Juan D. Guerra

Thomas Garbay

Guillaume Lajoie

Marco Bonizzato

Hierarchical Gaussian Process (H-GP) models divide problems into different subtasks, allowing for different models to address each part, mak… (voir plus)ing them well-suited for problems with inherent hierarchical structure. However, typical H-GP models do not fully take advantage of this structure, only sending information up or down the hierarchy. This one-way coupling limits sample efficiency and slows convergence. We propose Bidirectional Information Flow (BIF), an efficient H-GP framework that establishes bidirectional information exchange between parent and child models in H-GPs for online training. BIF retains the modular structure of hierarchical models - the parent combines subtask knowledge from children GPs - while introducing top-down feedback to continually refine children models during online learning. This mutual exchange improves sample efficiency, enables robust training, and allows modular reuse of learned subtask models. BIF outperforms conventional H-GP Bayesian Optimization methods, achieving up to 4x and 3x higher

2025-05-15

arXiv (Cornell University) (prépublication)

A flexible machine learning Mendelian randomization estimator applied to predict the safety and efficacy of sclerostin inhibition

Marc-André Legault

Jason Hartford

Benoit J. Arsenault

Archer Y. Yang

Joelle Pineau

2025-05-14

American Journal of Human Genetics (publié)