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arXiv Machine Learning By P. Harris, C. Bench, M. Rinkevi\v{c}ius, V. Marozas, L. Coquelin, A. Thompson, M. Nandi, U. Hackstein, P. J. Aston

Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals

Read the original on arXiv Machine Learning →

arXiv:2607. 19999v1 Announce Type: new Abstract: This Good Practice Guide presents work done in the QUMPHY project (Uncertainty quantification for machine learning models applied to photoplethysmography signals) that considered both machine learning and uncertainty quantification for problems which used photoplethysmography (PPG) signals from wearable devices as input.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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