3 citations · 9 across the 12 of their papers we have counts for
4 papers · 1 filter
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.…
Accurate simulation of operating system updates in neuroimaging using Monte-Carlo arithmetic
Ali Salari, Yohan Chatelain, Gregory Kiar +1
Operating system (OS) updates introduce numerical perturbations that impact the reproducibility of computational pipelines. In neuroimaging, this has important practical implicatio…
Reducing numerical precision preserves classification accuracy in Mondrian Forests
Marc Vicuna, Martin Khannouz, Gregory Kiar +2
Mondrian Forests are a powerful data stream classification method, but their large memory footprint makes them ill-suited for low-resource platforms such as connected objects. We e…