38 citations · 73 across the 31 of their papers we have counts for
6 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.…
The benefits of prefetching for large-scale cloud-based neuroimaging analysis workflows
Valerie Hayot-Sasson, Tristan Glatard, Ariel Rokem
To support the growing demands of neuroscience applications, researchers are transitioning to cloud computing for its scalable, robust and elastic infrastructure. Nevertheless, lar…
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…
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…
Modeling the Linux page cache for accurate simulation of data-intensive applications
Hoang-Dung Do, Valerie Hayot-Sasson, Rafael Ferreira da Silva +3
The emergence of Big Data in recent years has resulted in a growing need for efficient data processing solutions. While infrastructures with sufficient compute power are available,…