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Yohan Chatelain
Yohan Chatelain
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Titel
Zitiert von
Zitiert von
Jahr
Automatic exploration of reduced floating-point representations in iterative methods
Y Chatelain, E Petit, P de Oliveira Castro, G Lartigue, D Defour
Euro-Par 2019: Parallel Processing: 25th International Conference on …, 2019
332019
Numerical uncertainty in analytical pipelines lead to impactful variability in brain networks
G Kiar, Y Chatelain, P de Oliveira Castro, E Petit, A Rokem, G Varoquaux, ...
PloS one 16 (11), e0250755, 2021
14*2021
VeriTracer: Context-enriched tracer for floating-point arithmetic analysis
Y Chatelain, PDO Castro, E Petit, D Defour, J Bieder, M Torrent
2018 IEEE 25th Symposium on Computer Arithmetic (ARITH), 61-68, 2018
132018
Piecewise holistic autotuning of parallel programs with cere
M Popov, C Akel, Y Chatelain, W Jalby, P de Oliveira Castro
Concurrency and Computation: Practice and Experience 29 (15), e4190, 2017
112017
Accurate simulation of operating system updates in neuroimaging using Monte-Carlo arithmetic
A Salari, Y Chatelain, G Kiar, T Glatard
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and …, 2021
72021
PyTracer: Automatically profiling numerical instabilities in Python
Y Chatelain, NY Sao Young, G Kiar, T Glatard
IEEE Transactions on Computers, 2022
42022
Reducing numerical precision preserves classification accuracy in Mondrian Forests
M Vicuna, M Khannouz, G Kiar, Y Chatelain, T Glatard
2021 IEEE International Conference on Big Data (Big Data), 2785-2790, 2021
32021
Data augmentation through Monte Carlo arithmetic leads to more generalizable classification in connectomics
G Kiar, Y Chatelain, A Salari, AC Evans, T Glatard
arXiv preprint arXiv:2109.09649, 2021
32021
Numerical stability of DeepGOPlus inference
I Gonzalez Pepe, Y Chatelain, G Kiar, T Glatard
Plos one 19 (1), e0296725, 2024
22024
Reproducibility of tumor segmentation outcomes with a deep learning model
M Des Ligneris, A Bonnet, Y Chatelain, T Glatard, M Sdika, G Vila, ...
2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), 1-5, 2023
22023
Outils de débogage et d'optimisation des calculs flottants dans le contexte HPC
Y Chatelain
Université Paris Saclay (COmUE), 2019
22019
Numerical Uncertainty of Convolutional Neural Networks Inference for Structural Brain MRI Analysis
IG Pepe, V Sivakolunthu, HL Park, Y Chatelain, T Glatard
International Workshop on Uncertainty for Safe Utilization of Machine …, 2023
12023
The Impact of Hardware Variability on Applications Packaged with Docker and Guix: a Case Study in Neuroimaging
G Vila, E Medernach, I Gonzalez, A Bonnet, Y Chatelain, M Sdika, ...
2024
Longitudinal brain structure changes in Parkinson’s disease: A replication study
A Sokołowski, N Bhagwat, Y Chatelain, M Dugré, A Hanganu, O Monchi, ...
Plos one 19 (1), e0295069, 2024
2024
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IG Pepe, V Sivakolunthu, HL Park, Y Chatelain, T Glatard
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging: 5th …, 2023
2023
Numerical Uncertainty of Convolutional Neural Networks Inference for Structural Brain MRI Analysis
I Gonzalez Pepe, V Sivakolunthu, HL Park, Y Chatelain, T Glatard
arXiv e-prints, arXiv: 2308.01939, 2023
2023
A numerical variability approach to results stability tests and its application to neuroimaging
Y Chatelain, L Tetrel, CJ Markiewicz, M Goncalves, G Kiar, O Esteban, ...
arXiv preprint arXiv:2307.01373, 2023
2023
Predicting Parkinson's disease progression using MRI-based white matter radiomic biomarker and machine learning: a reproducibility and replicability study
M Arafe, NP Bhagwat, Y Chatelain, M Dugré, A Sokolowski, M Wang, ...
bioRxiv, 2023.05. 05.539590, 2023
2023
ReproVIP Report# 1.1. 1 Selected Metrics and Reproducibility Results
G Vila, M Des Ligneris, A Bonnet, T Glatard, Y Chatelain, C Frindel, ...
CREATIS Université Lyon 1, 2023
2023
Towards Abinit on ExaScale supercomputers: the challenge for electronic structure physicists
J Bieder, M Torrent, Y Chatelain
APS March Meeting Abstracts 2018, C34. 007, 2018
2018
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