60 citations · 72 across the 12 of their papers we have counts for
14 papers
The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)
Russell A. Poldrack, Christopher J. Markiewicz, Stefan Appelhoff +111
The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is mean…
A Parameter-efficient Multi-subject Model for Predicting fMRI Activity
Connor Lane, Gregory Kiar
This is the Algonauts 2023 submission report for team "BlobGPT". Our model consists of a multi-subject linear encoding head attached to a pretrained trunk model. The multi-subject…
Numerical Stability of DeepGOPlus Inference
Inés Gonzalez Pepe, Yohan Chatelain, Gregory Kiar +1
Convolutional neural networks (CNNs) are currently among the most widely-used deep neural network (DNN) architectures available and achieve state-of-the-art performance for many pr…
Pipeline-Invariant Representation Learning for Neuroimaging
Xinhui Li, Alex Fedorov, Mrinal Mathur +4
Deep learning has been widely applied in neuroimaging, including predicting brain-phenotype relationships from magnetic resonance imaging (MRI) volumes. MRI data usually requires e…
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…