Yohan Chatelain

According to our database, Yohan Chatelain authored at least 13 papers between 2017 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

PyTracer: Automatically Profiling Numerical Instabilities in Python.

Yohan Chatelain, Nigel Yong Sao Young, Gregory Kiar, Tristan Glatard

IEEE Trans. Computers, June, 2023

A numerical variability approach to results stability tests and its application to neuroimaging.

Yohan Chatelain, Loïc Tetrel, Christopher J. Markiewicz, Mathias Goncalves, Gregory Kiar, Oscar Esteban, Pierre Bellec, Tristan Glatard

CoRR, 2023

Numerical Uncertainty of Convolutional Neural Networks Inference for Structural Brain MRI Analysis.

Inés Gonzalez Pepe, Vinuyan Sivakolunthu, Hae Lang Park, Yohan Chatelain, Tristan Glatard

Proceedings of the Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, 2023

Reproducibility of Tumor Segmentation Outcomes with a Deep Learning Model.

Morgane Des Ligneris, Axel Bonnet, Yohan Chatelain, Tristan Glatard, Michaël Sdika, Gaël Vila, Valentine Wargnier-Dauchelle, Sorina Camarasu-Pop, Carole Frindel

Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, 2023

2022

Numerical Stability of DeepGOPlus Inference.

Inés Gonzalez Pepe, Yohan Chatelain, Gregory Kiar, Tristan Glatard

CoRR, 2022

2021

PyTracer: Automatically profiling numerical instabilities in Python.

Yohan Chatelain, Nigel Yong, Gregory Kiar, Tristan Glatard

CoRR, 2021

Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in Connectomics.

Gregory Kiar, Yohan Chatelain, Ali Salari, Alan C. Evans, Tristan Glatard

CoRR, 2021

Accurate Simulation of Operating System Updates in Neuroimaging Using Monte-Carlo Arithmetic.

Ali Salari, Yohan Chatelain, Gregory Kiar, Tristan Glatard

Proceedings of the Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis, 2021

Reducing numerical precision preserves classification accuracy in Mondrian Forests.

Marc Vicuna, Martin Khannouz, Gregory Kiar, Yohan Chatelain, Tristan Glatard

Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

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

PhD thesis, 2019

Automatic Exploration of Reduced Floating-Point Representations in Iterative Methods.

Yohan Chatelain, Eric Petit, Pablo de Oliveira Castro, Ghislain Lartigue, David Defour

Proceedings of the Euro-Par 2019: Parallel Processing, 2019

2018

VeriTracer: Context-enriched tracer for floating-point arithmetic analysis.

Yohan Chatelain, Pablo de Oliveira Castro, Eric Petit, David Defour, Jordan Bieder, Marc Torrent

Proceedings of the 25th IEEE Symposium on Computer Arithmetic, 2018

2017

Piecewise holistic autotuning of parallel programs with CERE.

Mihail Popov, Chadi Akel, Yohan Chatelain, William Jalby, Pablo de Oliveira Castro

Concurr. Comput. Pract. Exp., 2017