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Comparing perturbation models for evaluating stability of neuroimaging pipelines.

, , , , , , and . Int. J. High Perform. Comput. Appl., (2020)

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A Recommender System for Scientific Datasets and Analysis Pipelines., , and . WORKS, page 1-8. IEEE, (2021)Pipeline-Invariant Representation Learning for Neuroimaging., , , , , , and . CoRR, (2022)Reducing numerical precision preserves classification accuracy in Mondrian Forests., , , , and . IEEE BigData, page 2785-2790. IEEE, (2021)BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods., , , , , , , , , and 19 other author(s). PLoS Comput. Biol., (2017)Deploying large fixed file datasets with SquashFS and Singularity., , , , and . PEARC, page 72-76. ACM, (2020)Eliminating accidental deviations to minimize generalization error and maximize replicability: Applications in connectomics and genomics., , , , , , , , , and 6 other author(s). PLoS Comput. Biol., (2021)Accurate Simulation of Operating System Updates in Neuroimaging Using Monte-Carlo Arithmetic., , , and . UNSURE/PIPPI@MICCAI, volume 12959 of Lecture Notes in Computer Science, page 14-23. Springer, (2021)PyTracer: Automatically Profiling Numerical Instabilities in Python., , , and . IEEE Trans. Computers, 72 (6): 1792-1803 (June 2023)Comparing perturbation models for evaluating stability of neuroimaging pipelines., , , , , , and . Int. J. High Perform. Comput. Appl., (2020)