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20072023
most citedA Rigorous Uncertainty-Aware Quantification Framework Is Essential for Reproducible and Replicable Machine Learning Workflows

15 citations · 30 across the 6 of their papers we have counts for

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Showing 2018Show all

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cs.DC2018

Use Cases of Computational Reproducibility for Scientific Workflows at Exascale

Line Pouchard, Sterling Baldwin, Todd Elsethagen +6

We propose an approach for improved reproducibility that includes capturing and relating provenance characteristics and performance metrics, in a hybrid queriable system, the ProvE…