Web: http://arxiv.org/abs/2104.07651

June 17, 2022, 1:11 a.m. | Lukas Heumos, Philipp Ehmele, Luis Kuhn Cuellar, Kevin Menden, Edmund Miller, Steffen Lemke, Gisela Gabernet, Sven Nahnsen

cs.LG updates on arXiv.org arxiv.org

Machine learning has shown extensive growth in recent years and is now
routinely applied to sensitive areas. To allow appropriate verification of
predictive models before deployment, models must be deterministic. However,
major machine learning libraries default to the usage of non-deterministic
algorithms based on atomic operations. Solely fixing all random seeds is not
sufficient for deterministic machine learning. To overcome this shortcoming,
various machine learning libraries released deterministic counterparts to the
non-deterministic algorithms. We evaluated the effect of these algorithms …

arxiv framework learning machine machine learning

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