A systematic evaluation of uncertainty quantification techniques in deep learning: a case study in photoplethysmography signal analysis.
- DOI
- 10.1088/3049-477x/ae4c8e
- Published
- 2026 Jun 30
- Container
- Machine learning. Health
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1088/3049-477x/ae4c8e,
title = {A systematic evaluation of uncertainty quantification techniques in deep learning: a case study in photoplethysmography signal analysis.},
author = {Bench C and Pfeffer O and Desai V and Moulaeifard M and Coquelin L and Charlton PH and Strodthoff N and Hegemann N and Aston PJ and Thompson A},
year = {2026},
journal = {Machine learning. Health},
doi = {10.1088/3049-477x/ae4c8e},
url = {https://doi.org/10.1088/3049-477x/ae4c8e}
}RIS
TY - JOUR TI - A systematic evaluation of uncertainty quantification techniques in deep learning: a case study in photoplethysmography signal analysis. AU - Bench C AU - Pfeffer O AU - Desai V AU - Moulaeifard M AU - Coquelin L AU - Charlton PH AU - Strodthoff N AU - Hegemann N AU - Aston PJ AU - Thompson A PY - 2026 JO - Machine learning. Health DO - 10.1088/3049-477x/ae4c8e UR - https://doi.org/10.1088/3049-477x/ae4c8e ER -
APA
C, B., O, P., V, D., M, M., L, C., PH, C., N, S., N, H., PJ, A., & A, T. (2026). A systematic evaluation of uncertainty quantification techniques in deep learning: a case study in photoplethysmography signal analysis.. Machine learning. Health. https://doi.org/10.1088/3049-477x/ae4c8e
Source records
- pubmed · retrieved 2026-09-27T09:55:04.811Z