A rigorous uncertainty-aware quantification framework is essential for reproducible and replicable machine learning workflows
In: Digital discovery, Jg. 2 (2023), Heft 5, S. 1251-1258
serialPeriodical
Zugriff:
Titel: |
A rigorous uncertainty-aware quantification framework is essential for reproducible and replicable machine learning workflows
|
---|---|
Autor/in / Beteiligte Person: | Pouchard, Line ; Reyes, Kristofer G. ; Alexander, Francis J. ; Yoon, Byung-Jun |
Link: | |
Zeitschrift: | Digital discovery, Jg. 2 (2023), Heft 5, S. 1251-1258 |
Veröffentlichung: | 2023 |
Medientyp: | serialPeriodical |
ISSN: | 2635-098X (print) |
Sonstiges: |
|