Publications

Neural Kinematic Bases for Fluids
Yibo Liu
Paul Kry
Kenny Erleben
Noam Aigerman
Sune Darkner
Teseo Schneider
2025-04-01
arXiv (publié)
doi.org
arxiv.org
Online Interior-point Methods for Time-varying Equality-constrained Optimization
Jean-Luc Lupien
Iman Shames
Antoine Lesage-Landry
2025-04-01
IEEE Transactions on Automatic Control (publié)
doi.org
arxiv.org
Performance Smells in ML and Non-ML Python Projects: A Comparative Study
Franccois Belias
Leuson Da Silva
Foutse Khomh
Cyrine Zid
2025-04-01
arXiv (publié)
doi.org
arxiv.org
Prism: Dynamic and Flexible Benchmarking of LLMs Code Generation with Monte Carlo Tree Search
Vahid Majdinasab
Amin Nikanjam
Foutse Khomh
2025-04-01
arXiv (publié)
doi.org
arxiv.org
Progressive Multi-Source Domain Adaptation for Personalized Facial Expression Recognition
Muhammad Osama Zeeshan
Marco Pedersoli
Alessandro Lameiras Koerich
Eric Grange
2025-04-01
arXiv (publié)
doi.org
arxiv.org
Scaling Language-Free Visual Representation Learning
David Fan
Shengbang Tong
Jiachen Zhu
Koustuv Sinha
Zhuang Liu
Xinlei Chen
Michael Rabbat
Nicolas Ballas
Yann LeCun
Amir Bar
Saining Xie
2025-04-01
arXiv (publié)
doi.org
arxiv.org
Scaling Language-Free Visual Representation Learning
David Fan
Shengbang Tong
Jiachen Zhu
Koustuv Sinha
Zhuang Liu
Xinlei Chen
Michael Rabbat
Nicolas Ballas
Yann LeCun
Amir Bar
Saining Xie
2025-04-01
ArXiv (prépublication)
arxiv.org
arxiv.org
Semantic Commit: Helping Users Update Intent Specifications for AI Memory at Scale
Priyan Vaithilingam
Munyeong Kim
Frida-Cecilia Acosta-Parenteau
Daniel Lee
Amine Mhedhbi
Elena L. Glassman
Ian Arawjo
2025-04-01
arXiv (publié)
doi.org
arxiv.org
Sliced-Wasserstein Distance-based Data Selection
Julien Pallage
Antoine Lesage-Landry
We propose a new unsupervised anomaly detection method based on the sliced-Wasserstein distance for training data selection in machine learn… (voir plus)ing approaches. Our filtering technique is interesting for decision-making pipelines deploying machine learning models in critical sectors, e.g., power systems, as it offers a conservative data selection and an optimal transport interpretation. To ensure the scalability of our method, we provide two efficient approximations. The first approximation processes reduced-cardinality representations of the datasets concurrently. The second makes use of a computationally light Euclidian distance approximation. Additionally, we open the first dataset showcasing localized critical peak rebate demand response in a northern climate. We present the filtering patterns of our method on synthetic datasets and numerically benchmark our method for training data selection. Finally, we employ our method as part of a first forecasting benchmark for our open-source dataset.
2025-04-01
arXiv (publié)
doi.org
arxiv.org
TAPNext: Tracking Any Point (TAP) as Next Token Prediction
Artem Zholus
Carl Doersch
Yi Yang
Skanda Koppula
Viorica Patraucean
Xu Owen He
Ignacio Rocco
Mehdi S. M. Sajjadi
Sarath Chandar
Ross Goroshin
2025-04-01
arXiv (publié)
doi.org
arxiv.org
Towards Assessing Deep Learning Test Input Generators
Seif Mzoughi
Ahmed Haj Yahmed
Mohamed Elshafei
Foutse Khomh
Diego Elias Costa
2025-04-01
arXiv (publié)
doi.org
arxiv.org
Trade‐off of different deep learning‐based auto‐segmentation approaches for treatment planning of pediatric craniospinal irradiation autocontouring of OARs for pediatric CSI
Alana Thibodeau‐Antonacci
Marija Popovic
Ozgur Ates
Chia‐Ho Hua
James Schneider
Sonia Skamene
Carolyn Freeman
Shirin A. Enger
James Man Git Tsui
As auto‐segmentation tools become integral to radiotherapy, more commercial products emerge. However, they may not always suit our needs. … (voir plus)One notable example is the use of adult‐trained commercial software for the contouring of organs at risk (OARs) of pediatric patients.
2025-04-01
Medical Physics (Lancaster) (publié)
doi.org