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ORCIDYohan Chatelain
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2020 – today
- 2024
[c7]Gaël Vila
, Emmanuel Medernach, Inés Gonzalez Pepe, Axel Bonnet, Yohan Chatelain, Michaël Sdika, Tristan Glatard, Sorina Camarasu-Pop:
The Impact of Hardware Variability on Applications Packaged with Docker and Guix: a Case Study in Neuroimaging. ACM-REP 2024- [i6]Mathieu Dugré, Yohan Chatelain, Tristan Glatard:
An Analysis of Performance Bottlenecks in MRI Pre-Processing. CoRR abs/2405.17650 (2024) - 2023
- [j2]Yohan Chatelain, Nigel Yong Sao Young, Gregory Kiar, Tristan Glatard:
PyTracer: Automatically Profiling Numerical Instabilities in Python. IEEE Trans. Computers 72(6): 1792-1803 (2023) - [c6]Morgane Des Ligneris, Axel Bonnet, Yohan Chatelain, Tristan Glatard, Michaël Sdika, Gaël Vila, Valentine Wargnier-Dauchelle, Sorina Camarasu-Pop, Carole Frindel:
Reproducibility of Tumor Segmentation Outcomes with a Deep Learning Model. ISBI 2023: 1-5 - [c5]Inés Gonzalez Pepe, Vinuyan Sivakolunthu, Hae Lang Park, Yohan Chatelain, Tristan Glatard:
Numerical Uncertainty of Convolutional Neural Networks Inference for Structural Brain MRI Analysis. UNSURE@MICCAI 2023: 64-73 - [i5]Yohan Chatelain, Loïc Tetrel, Christopher J. Markiewicz, Mathias Goncalves, Gregory Kiar, Oscar Esteban, Pierre Bellec, Tristan Glatard:
A numerical variability approach to results stability tests and its application to neuroimaging. CoRR abs/2307.01373 (2023) - 2022
- [i4]Inés Gonzalez Pepe, Yohan Chatelain, Gregory Kiar, Tristan Glatard:
Numerical Stability of DeepGOPlus Inference. CoRR abs/2212.06361 (2022) - 2021
- [c4]Marc Vicuna, Martin Khannouz, Gregory Kiar, Yohan Chatelain, Tristan Glatard:
Reducing numerical precision preserves classification accuracy in Mondrian Forests. IEEE BigData 2021: 2785-2790 - [c3]Ali Salari, Yohan Chatelain, Gregory Kiar, Tristan Glatard:
Accurate Simulation of Operating System Updates in Neuroimaging Using Monte-Carlo Arithmetic. UNSURE/PIPPI@MICCAI 2021: 14-23 - [i3]Marc Vicuna, Martin Khannouz, Gregory Kiar, Yohan Chatelain, Tristan Glatard:
Reducing numerical precision preserves classification accuracy in Mondrian Forests. CoRR abs/2106.14340 (2021) - [i2]Gregory Kiar, Yohan Chatelain, Ali Salari, Alan C. Evans, Tristan Glatard:
Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in Connectomics. CoRR abs/2109.09649 (2021) - [i1]Yohan Chatelain, Nigel Yong, Gregory Kiar, Tristan Glatard:
PyTracer: Automatically profiling numerical instabilities in Python. CoRR abs/2112.11508 (2021)
2010 – 2019
- 2019
- [b1]Yohan Chatelain:
Outils de débogage et d'optimisation des calculs flottants dans un contexte HPC. (Tools for debugging and optimizing floating-point computations in HPC). University of Paris-Saclay, France, 2019 - [c2]Yohan Chatelain, Eric Petit, Pablo de Oliveira Castro, Ghislain Lartigue, David Defour:
Automatic Exploration of Reduced Floating-Point Representations in Iterative Methods. Euro-Par 2019: 481-494 - 2018
- [c1]Yohan Chatelain, Pablo de Oliveira Castro, Eric Petit, David Defour, Jordan Bieder, Marc Torrent:
VeriTracer: Context-enriched tracer for floating-point arithmetic analysis. ARITH 2018: 61-68 - 2017
- [j1]Mihail Popov, Chadi Akel, Yohan Chatelain, William Jalby, Pablo de Oliveira Castro:
Piecewise holistic autotuning of parallel programs with CERE. Concurr. Comput. Pract. Exp. 29(15) (2017)
Coauthor Index
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