Yohan Chatelain

21 papers C 1Journal 12Unranked 7
YearRankTypeTitle / Venue / Authors
2026 J jnl
Conservative & Aggressive NaNs Accelerate U-Nets for Neuroimaging.
CoRR
Inés Gonzalez Pepe, Vinuyan Sivakolunthu, Jacob Fortin, Yohan Chatelain, Tristan Glatard
2026 J jnl
Fuzzy PyTorch: Rapid Numerical Variability Evaluation for Deep Learning Models.
Trans. Mach. Learn. Res.
Inés Gonzalez Pepe, Hiba Akhaddar, Tristan Glatard, Yohan Chatelain
2025 J jnl
A Numerical Variability Approach to Results Stability Tests and Its Application to Neuroimaging.
IEEE Trans. Computers
Yohan Chatelain, Loïc Tetrel, Christopher J. Markiewicz, Mathias Goncalves, Gregory Kiar, Oscar Esteban, Pierre Bellec, Tristan Glatard
2025 — conf
Numerical Uncertainty in Linear Registration: An Experimental Study.
UNSURE@MICCAI
Niusha Mirhakimi, Yohan Chatelain, Jean-Baptiste Poline, Tristan Glatard
2025 J jnl
Uncertain but Useful: Leveraging CNN Variability into Data Augmentation.
CoRR
Inés Gonzalez Pepe, Vinuyan Sivakolunthu, Yohan Chatelain, Tristan Glatard
2024 J jnl
An Analysis of Performance Bottlenecks in MRI Pre-Processing.
CoRR
Mathieu Dugré, Yohan Chatelain, Tristan Glatard
2024 — conf
The Impact of Hardware Variability on Applications Packaged with Docker and Guix: a Case Study in Neuroimaging.
ACM-REP
Gaël Vila, Emmanuel Medernach, Inés Gonzalez Pepe, Axel Bonnet, Yohan Chatelain, Michaël Sdika, Tristan Glatard, Sorina Camarasu-Pop
2023 J jnl
A numerical variability approach to results stability tests and its application to neuroimaging.
CoRR
Yohan Chatelain, Loïc Tetrel, Christopher J. Markiewicz, Mathias Goncalves, Gregory Kiar, Oscar Esteban, Pierre Bellec, Tristan Glatard
2023 — conf
Numerical Uncertainty of Convolutional Neural Networks Inference for Structural Brain MRI Analysis.
UNSURE@MICCAI
Inés Gonzalez Pepe, Vinuyan Sivakolunthu, Hae Lang Park, Yohan Chatelain, Tristan Glatard
2023 J jnl
PyTracer: Automatically Profiling Numerical Instabilities in Python.
IEEE Trans. Computers
Yohan Chatelain, Nigel Yong Sao Young, Gregory Kiar, Tristan Glatard
2023 — conf
Reproducibility of Tumor Segmentation Outcomes with a Deep Learning Model.
ISBI
Morgane Des Ligneris, Axel Bonnet, Yohan Chatelain, Tristan Glatard, Michaël Sdika, Gaël Vila, Valentine Wargnier-Dauchelle, Sorina Camarasu-Pop, Carole Frindel
2022 J jnl
Numerical Stability of DeepGOPlus Inference.
CoRR
Inés Gonzalez Pepe, Yohan Chatelain, Gregory Kiar, Tristan Glatard
2021 — conf
Accurate Simulation of Operating System Updates in Neuroimaging Using Monte-Carlo Arithmetic.
UNSURE/PIPPI@MICCAI
Ali Salari, Yohan Chatelain, Gregory Kiar, Tristan Glatard
2021 J jnl
Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in Connectomics.
CoRR
Gregory Kiar, Yohan Chatelain, Ali Salari, Alan C. Evans, Tristan Glatard
2021 J jnl
PyTracer: Automatically profiling numerical instabilities in Python.
CoRR
Yohan Chatelain, Nigel Yong, Gregory Kiar, Tristan Glatard
2021 — conf
Reducing numerical precision preserves classification accuracy in Mondrian Forests.
IEEE BigData
Marc Vicuna, Martin Khannouz, Gregory Kiar, Yohan Chatelain, Tristan Glatard
2021 J jnl
Reducing numerical precision preserves classification accuracy in Mondrian Forests.
CoRR
Marc Vicuna, Martin Khannouz, Gregory Kiar, Yohan Chatelain, Tristan Glatard
2019 — conf
Automatic Exploration of Reduced Floating-Point Representations in Iterative Methods.
Euro-Par
Yohan Chatelain, Eric Petit, Pablo de Oliveira Castro, Ghislain Lartigue, David Defour
2019 — —
Outils de débogage et d'optimisation des calculs flottants dans un contexte HPC. (Tools for debugging and optimizing floating-point computations in HPC).
Yohan Chatelain
2018 C conf
VeriTracer: Context-enriched tracer for floating-point arithmetic analysis.
ARITH
Yohan Chatelain, Pablo de Oliveira Castro, Eric Petit, David Defour, Jordan Bieder, Marc Torrent
2017 J jnl
Piecewise holistic autotuning of parallel programs with CERE.
Concurr. Comput. Pract. Exp.
Mihail Popov, Chadi Akel, Yohan Chatelain, William Jalby, Pablo de Oliveira Castro