4 citations · 4 across the 5 of their papers we have counts for
6 papers
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.…
File-based localization of numerical perturbations in data analysis pipelines
Ali Salari, Gregory Kiar, Lindsay Lewis +2
Data analysis pipelines are known to be impacted by computational conditions, presumably due to the creation and propagation of numerical errors. While this process could play a ma…
Deploying large fixed file datasets with SquashFS and Singularity
Pierre Rioux, Gregory Kiar, Alexandre Hutton +2
Shared high-performance computing (HPC) platforms, such as those provided by XSEDE and Compute Canada, enable researchers to carry out large-scale computational experiments at a fr…
Comparing Perturbation Models for Evaluating Stability of Neuroimaging Pipelines
Gregory Kiar, Pablo de Oliveira Castro, Pierre Rioux +4
A lack of software reproducibility has become increasingly apparent in the last several years, calling into question the validity of scientific findings affected by published tools…
NeuroStorm: Accelerating Brain Science Discovery in the Cloud
Gregory Kiar, Robert J. Anderson, Alex Baden +24
Neuroscientists are now able to acquire data at staggering rates across spatiotemporal scales. However, our ability to capitalize on existing datasets, tools, and intellectual capa…
Grand Challenges for Global Brain Sciences
Joshua T. Vogelstein, Katrin Amunts, Andreas Andreou +59
The next grand challenges for society and science are in the brain sciences. A collection of 60+ scientists from around the world, together with 10+ observers from national, privat…