Publications

Proceedings of the OHBM Open Science Room 2024
Selma Lugtmeijer
Ju-Chi Yu
Xiangzhen Kong
Lune Bellec
Janine D. Bijsterbosch
Elizabeth DuPre
Oscar Esteban
Ibrahim Faye
Seok-Jun Hong
Chuan-Peng Hu
Shella Keilholz
Chun-Chia Kung
Hyeong Hun Lee
Daniel Margulies
Cyril Pernet
Franco Pestilli
Jean-Baptiste Poline
Pradeep R. Raamana
Francesco Santini
Won Mok Shim … (voir 30 de plus)
Paul M. Thompson
Chao-Gan Yan
Niall W. Duncan
Nikhil Bhagwat
Peter Fox
Ana Van Gulick
David N. Kennedy
Gorana Pobric
Neda Sadeghi
Nick Souter
Sandeep Panta
Isabelle van der Velpen
Tonya White
Sina Mansour L.
Qing Wang
Povilas Karvelis
Anibal S. Heinsfeld
Yu-Fang Yang
Hong Ji Kim
Nur Shahidatul Nabila Binti Ibrahim
Stefano Moia
Wei Zhang
Jessica Haigh
Rose-Marie Kouwenhoven
Terra Hyun Lee
Hurshitha Vasudevan
Yuping Yang
Subapriya Suppiah
Yi-Ju Lee
Nils Muhlert
2025-08-18
Aperture Neuro (publié)
doi.org
MuSACo: Multimodal Subject-Specific Selection and Adaptation for Expression Recognition with Co-Training
Muhammad Osama Zeeshan
Natacha Gillet
Alessandro Lameiras Koerich
Marco Pedersoli
Francois Bremond
Eric Granger
2025-08-17
ArXiv (prépublication)
arxiv.org
arxiv.org
Increasing the Utility of Synthetic Images through Chamfer Guidance
Nicola Dall'Asen
Xiaofeng Zhang
Reyhane Askari Hemmat
Melissa Hall
Jakob Verbeek
Adriana Romero Soriano
Michal Drozdzal
Conditional image generative models hold considerable promise to produce infinite amounts of synthetic training data. Yet, recent progress i… (voir plus)n generation quality has come at the expense of generation diversity, limiting the utility of these models as a source of synthetic training data. Although guidance-based approaches have been introduced to improve the utility of generated data by focusing on quality or diversity, the (implicit or explicit) utility functions oftentimes disregard the potential distribution shift between synthetic and real data. In this work, we introduce Chamfer Guidance: a training-free guidance approach which leverages a handful of real exemplar images to characterize the quality and diversity of synthetic data. We show that by leveraging the proposed Chamfer Guidance, we can boost the diversity of the generations w.r.t. a dataset of real images while maintaining or improving the generation quality on ImageNet-1k and standard geo-diversity benchmarks. Our approach achieves state-of-the-art few-shot performance with as little as 2 exemplar real images, obtaining 96.4\% in terms of precision, and 86.4\% in terms of distributional coverage, which increase to 97.5\% and 92.7\%, respectively, when using 32 real images. We showcase the benefits of the Chamfer Guidance generation by training downstream image classifiers on synthetic data, achieving accuracy boost of up to 15\% for in-distribution over the baselines, and up to 16\% in out-of-distribution. Furthermore, our approach does not require using the unconditional model, and thus obtains a 31\% reduction in FLOPs w.r.t. classifier-free-guidance-based approaches at sampling time.
2025-08-14
ArXiv (prépublication)
arxiv.org
arxiv.org
Street Review: A Participatory AI-Based Framework for Assessing Streetscape Inclusivity
Rashid A. Mushkani
Shin (Alexandre) Koseki
2025-08-14
ArXiv (prépublication)
arxiv.org
arxiv.org
The Interpolation Constraint in the RV Analysis of M-Dwarfs Using Empirical Templates
Dhvani Doshi
Nicolas B. Cowan
'Etienne Artigau
René Doyon
Andr'e M. Silva
K. A. Moulla
Yashar Hezaveh
Precise radial velocity (pRV) measurements of M-dwarfs in the near-infrared (NIR) rely on empirical templates due to the lack of accurate st… (voir plus)ellar spectral models in this regime. Templates are assumed to approximate the true spectrum when constructed from many observations or in the high signal-to-noise limit. We develop a numerical simulation that generates SPIRou-like pRV observations from PHOENIX spectra, constructs empirical templates, and estimates radial velocities. This simulation solely considers photon noise and evaluates when empirical templates remain reliable for pRV analysis. Our results reveal a previously unrecognized noise source in templates, establishing a fundamental floor for template-based pRV measurements. We find that templates inherently include distortions in stellar line shapes due to imperfect interpolation at the detector's sampling resolution. The magnitude of this interpolation error depends on sampling resolution and RV content. Consequently, while stars with a higher RV content, such as cooler M-dwarfs are expected to yield lower RV uncertainties, their dense spectral features can amplify interpolation errors, potentially biasing RV estimates. For a typical M4V star, SPIRou's spectral and sampling resolution imposes an RV uncertainty floor of 0.5-0.8 m/s, independent of the star's magnitude or the telescope's aperture. These findings reveal a limitation of template-based pRV methods, underscoring the need for improved spectral modeling and better-than-Nyquist detector sampling to reach the next level of RV precision.
2025-08-14
ArXiv (prépublication)
arxiv.org
arxiv.org
Beyond Na\"ive Prompting: Strategies for Improved Zero-shot Context-aided Forecasting with LLMs
Arjun Ashok
Andrew Robert Williams
Vincent Zhihao Zheng
Irina Rish
Nicolas Chapados
Étienne Marcotte
Valentina Zantedeschi
Alexandre Drouin
Forecasting in real-world settings requires models to integrate not only historical data but also relevant contextual information, often ava… (voir plus)ilable in textual form. While recent work has shown that large language models (LLMs) can be effective context-aided forecasters via na\"ive direct prompting, their full potential remains underexplored. We address this gap with 4 strategies, providing new insights into the zero-shot capabilities of LLMs in this setting. ReDP improves interpretability by eliciting explicit reasoning traces, allowing us to assess the model's reasoning over the context independently from its forecast accuracy. CorDP leverages LLMs solely to refine existing forecasts with context, enhancing their applicability in real-world forecasting pipelines. IC-DP proposes embedding historical examples of context-aided forecasting tasks in the prompt, substantially improving accuracy even for the largest models. Finally, RouteDP optimizes resource efficiency by using LLMs to estimate task difficulty, and routing the most challenging tasks to larger models. Evaluated on different kinds of context-aided forecasting tasks from the CiK benchmark, our strategies demonstrate distinct benefits over na\"ive prompting across LLMs of different sizes and families. These results open the door to further simple yet effective improvements in LLM-based context-aided forecasting.
2025-08-13
ArXiv (prépublication)
arxiv.org
arxiv.org
Nested-ReFT: Efficient Reinforcement Learning for Large Language Model Fine-Tuning via Off-Policy Rollouts
Maxime Heuillet
Yufei CUI
Boxing Chen
Audrey Durand
Prasanna Parthasarathi
2025-08-13
ArXiv (prépublication)
arxiv.org
arxiv.org
Pathfinding: a neurodynamical account of intuition
Steven Kotler
Michael Mannino
Karl Friston
Gyorgy Buzsáki
J. A. Scott Kelso
Guillaume Dumas
2025-08-13
Communications Biology (publié)
doi.org
WeDesign: Generative AI-Facilitated Community Consultations for Urban Public Space Design
Rashid A. Mushkani
Hugo Berard
Shin (Alexandre) Koseki
2025-08-13
ArXiv (prépublication)
arxiv.org
arxiv.org
A Guide to Robust Generalization: The Impact of Architecture, Pre-training, and Optimization Strategy
Maxime Heuillet
Rishika Bhagwatkar
Jonas Ngnawe
Yann Batiste Pequignot
Alexandre Larouche
Christian Gagné
Irina Rish
Ola Ahmad
Audrey Durand
Deep learning models operating in the image domain are vulnerable to small input perturbations. For years, robustness to such perturbations … (voir plus)was pursued by training models from scratch (i.e., with random initializations) using specialized loss objectives. Recently, robust fine-tuning has emerged as a more efficient alternative: instead of training from scratch, pretrained models are adapted to maximize predictive performance and robustness. To conduct robust fine-tuning, practitioners design an optimization strategy that includes the model update protocol (e.g., full or partial) and the specialized loss objective. Additional design choices include the architecture type and size, and the pretrained representation. These design choices affect robust generalization, which is the model's ability to maintain performance when exposed to new and unseen perturbations at test time. Understanding how these design choices influence generalization remains an open question with significant practical implications. In response, we present an empirical study spanning 6 datasets, 40 pretrained architectures, 2 specialized losses, and 3 adaptation protocols, yielding 1,440 training configurations and 7,200 robustness measurements across five perturbation types. To our knowledge, this is the most diverse and comprehensive benchmark of robust fine-tuning to date. While attention-based architectures and robust pretrained representations are increasingly popular, we find that convolutional neural networks pretrained in a supervised manner on large datasets often perform best. Our analysis both confirms and challenges prior design assumptions, highlighting promising research directions and offering practical guidance.
2025-08-12
ArXiv (prépublication)
arxiv.org
arxiv.org
The Impact of a Pediatric Surgery Fundamentals Boot Camp on New Surgical Trainees' Perceived Knowledge and Confidence Levels.
Julia Ferreira
Simon Rahman
Fabio Botelho
Farhan Banji
W. A. Igrine
Gianluca Bertolizio
Sam Daniel
Thomas Engelhardt
Chantal Frigon
Lily H P Nguyen
Catherine Paquet
Dan Poenaru
Pramod Puligandla
Hussein Wissanji
Davinia Withington
Yasmine Yousef
Sherif Emil
2025-08-12
Journal of Pediatric Surgery (publié)
doi.org
FairFLRep: Fairness aware fault localization and repair of Deep Neural Networks
Moses Openja
Paolo Arcaini
Foutse Khomh
Fuyuki Ishikawa
2025-08-11
ArXiv (prépublication)
arxiv.org
arxiv.org