Uncertainty quantification in Neural Networks by Approximate Bayesian Computation: Application to fatigue in composite materials

Modern machine learning algorithms excel in a great variety of tasks, but at the same time, it is also known that those complex models need to deal with uncertainty from different sources. Consequently, understanding if the model is indeed making accurate predictions or simply guessing at random is...

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Vydáno v:	Engineering applications of artificial intelligence Ročník 107; s. 104511
Hlavní autoři:	Fernández, Juan, Chiachío, Manuel, Chiachío, Juan, Muñoz, Rafael, Herrera, Francisco
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.01.2022
Témata:	Approximate Bayesian Computation Bayesian Neural Network Gradient-free training Non-parametric formulation Subset Simulation Uncertainty quantification Approximate Bayesian Computation Uncertainty quantification Subset Simulation Gradient-free training Bayesian Neural Network Non-parametric formulation
ISSN:	0952-1976, 1873-6769
On-line přístup:	Získat plný text
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