Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures

Uncertainty quantification for complex deep learning models is increasingly important as these techniques see growing use in high-stakes, real-world settings. Currently, the quality of a model’s uncertainty is evaluated using point-prediction metrics, such as the negative log-likelihood (NLL), expec...

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Bibliografiske detaljer

Principais autores:	Benjamin Kompa, Jasper Snoek, Andrew L. Beam
Format:	Artigo
Sprog:	Inglês
Udgivet:	MDPI AG 2021-11-01
Serier:	Entropy
Fag:	uncertainty quantification coverage Bayesian methods dataset shift
Online adgang:	https://www.mdpi.com/1099-4300/23/12/1608
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