Ranking Information Extracted from Uncertainty Quantification of the Prediction of a Deep Learning Model on Medical Time Series Data

Uncertainty quantification in deep learning models is especially important for the medical applications of this complex and successful type of neural architectures. One popular technique is Monte Carlo dropout that gives a sample output for a record, which can be measured statistically in terms of a...

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Detalhes bibliográficos

Principais autores:	Ruxandra Stoean, Catalin Stoean, Miguel Atencia, Roberto Rodríguez-Labrada, Gonzalo Joya
Formato:	Artigo
Idioma:	Inglês
Publicado em:	MDPI AG 2020-07-01
coleção:	Mathematics
Assuntos:	deep learning time series uncertainty quantification Monte Carlo dropout random forest
Acesso em linha:	https://www.mdpi.com/2227-7390/8/7/1078
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