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...

全面介紹

書目詳細資料

Principais autores:	Ruxandra Stoean, Catalin Stoean, Miguel Atencia, Roberto Rodríguez-Labrada, Gonzalo Joya
格式:	Artigo
語言:	Inglês
出版:	MDPI AG 2020-07-01
叢編:	Mathematics
主題:	deep learning time series uncertainty quantification Monte Carlo dropout random forest
在線閱讀:	https://www.mdpi.com/2227-7390/8/7/1078
標簽:	添加標簽 沒有標簽, 成為第一個標記此記錄!