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pro vyhledávání: '"Molina, María"'
Estimating and disentangling epistemic uncertainty (uncertainty that can be reduced with more training data) and aleatoric uncertainty (uncertainty that is inherent to the task at hand) is critically important when applying machine learning (ML) to h
Externí odkaz:
http://arxiv.org/abs/2402.03478
Autor:
Oskar Jacek Rojewski
Publikováno v:
Philostrato, Vol 0, Iss 4, Pp 102-106 (2018)
Externí odkaz:
https://doaj.org/article/015413a8708d443f9c41958b8147ff05
Autor:
Schreck, John S., Gagne II, David John, Becker, Charlie, Chapman, William E., Elmore, Kim, Fan, Da, Gantos, Gabrielle, Kim, Eliot, Kimpara, Dhamma, Martin, Thomas, Molina, Maria J., Pryzbylo, Vanessa M., Radford, Jacob, Saavedra, Belen, Willson, Justin, Wirz, Christopher
Robust quantification of predictive uncertainty is critical for understanding factors that drive weather and climate outcomes. Ensembles provide predictive uncertainty estimates and can be decomposed physically, but both physics and machine learning
Externí odkaz:
http://arxiv.org/abs/2309.13207
Autor:
Andersson, Jakob
Publikováno v:
Wiener Zeitschrift für die Kunde des Morgenlandes, 2016 Jan 01. 106, 301-305.
Externí odkaz:
https://www.jstor.org/stable/26449359
Autor:
Montesdeoca Calderon, María Gabriela, Gil-Saura, Irene, Ruiz-Molina, María-Eugenia, Martin-Rios, Carlos
Publikováno v:
British Food Journal, 2023, Vol. 126, Issue 1, pp. 471-488.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/BFJ-01-2023-0023
Autor:
Yovana Celaya Nandez
Publikováno v:
América Latina en la Historia Económica, Vol 23, Iss 1 (2016)
No contiene resumen.
Externí odkaz:
https://doaj.org/article/71a15501381c4f52be70c739d822b32b
Autor:
Sigrist, Marcel
Publikováno v:
Revue Biblique (1946-), 2015 Jul 01. 122(3), 464-464.
Externí odkaz:
https://www.jstor.org/stable/44092364