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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...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8700765/ https://www.ncbi.nlm.nih.gov/pubmed/34945914 http://dx.doi.org/10.3390/e23121608 |