Search Results for author:
Found 9 papers, 5 papers with code
The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)
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.
A Parameter-efficient Multi-subject Model for Predicting fMRI Activity
This is the Algonauts 2023 submission report for team "BlobGPT".
Numerical Stability of DeepGOPlus Inference
However, recent works have highlighted numerical stability challenges in DNNs, which also relates to their known sensitivity to noise injection.
Pipeline-Invariant Representation Learning for Neuroimaging
We next propose two pipeline-invariant representation learning methodologies, MPSL and PXL, to improve robustness in classification performance and to capture similar neural network representations.
Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in Connectomics
The variability in the perturbed networks was captured in an augmented dataset, which was then used for an age classification task.
A Recommender System for Scientific Datasets and Analysis Pipelines
We investigate the feasibility of a collaborative filtering system to recommend pipelines and datasets based on provenance records from previous executions.
Accurate simulation of operating system updates in neuroimaging using Monte-Carlo arithmetic
Overall, our results establish that FL accurately simulates results variability due to OS updates, and is a practical framework to quantify numerical uncertainty in neuroimaging.
Reducing numerical precision preserves classification accuracy in Mondrian Forests
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.
NeuroStorm: Accelerating Brain Science Discovery in the Cloud
Neuroscientists are now able to acquire data at staggering rates across spatiotemporal scales.
