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Stoean et al., 2020 - Google Patents

Ranking information extracted from uncertainty quantification of the prediction of a deep learning model on medical time series data

Stoean et al., 2020

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Document ID
9393981159476069219
Author
Stoean R
Stoean C
Atencia M
Rodríguez-Labrada R
Joya G
Publication year
Publication venue
Mathematics

External Links

Snippet

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 …
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Classifications

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    • G06F19/345—Medical expert systems, neural networks or other automated diagnosis
    • G—PHYSICS
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    • G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
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