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Examining the Effect of Implementation Factors on Deep Learning Reproducibility
To account for these implementation factors, researchers should run their experiments multiple times in different hardware and software environments to verify their conclusions are not affected.
Sources of Irreproducibility in Machine Learning: A Review
Objective: The objective of this paper is to develop a framework that enable applied data science practitioners and researchers to understand which experiment design choices can lead to false findings and how and by this help in analyzing the conclusions of reproducibility experiments.
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