3 citations · 4 across the 4 of their papers we have counts for
4 papers
A numerical variability approach to results stability tests and its application to neuroimaging
Yohan Chatelain, Loïc Tetrel, Christopher J. Markiewicz +5
Ensuring the long-term reproducibility of data analyses requires results stability tests to verify that analysis results remain within acceptable variation bounds despite inevitabl…
PyTracer: Automatically profiling numerical instabilities in Python
Yohan Chatelain, Nigel Yong, Gregory Kiar +1
Numerical stability is a crucial requirement of reliable scientific computing. However, despite the pervasiveness of Python in data science, analyzing large Python programs remains…
Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in Connectomics
Gregory Kiar, Yohan Chatelain, Ali Salari +2
Machine learning models are commonly applied to human brain imaging datasets in an effort to associate function or structure with behaviour, health, or other individual phenotypes.…
A Recommender System for Scientific Datasets and Analysis Pipelines
Mandana Mazaheri, Gregory Kiar, Tristan Glatard
Scientific datasets and analysis pipelines are increasingly being shared publicly in the interest of open science. However, mechanisms are lacking to reliably identify which pipeli…