|Title:||Expressing uncertainty in neural networks for production systems||Authors:||Multaheb, Samim Ahmad
|Language:||en||Subject (DDC):||DDC::000 Informatik, Informationswissenschaft, allgemeine Werke::000 Informatik, Wissen, Systeme||Issue Date:||2021||Document Type:||Article||Journal / Series / Working Paper (HSU):||Automatisierungstechnik : AT||Volume:||69||Issue:||3||Page Start:||221||Page End:||230||Abstract:||
The application of machine learning, especially of trained neural networks, requires a high level of trust in their results. A key to this trust is the network's ability to assess the uncertainty of the computed results. This is a prerequisite for the use of such networks in closed-control loops and in automation systems. This paper describes approaches for enabling neural networks to automatically learn the uncertainties of their results.
|Organization Units (connected with the publication):||Informatik im Maschinenbau||URL:||https://api.elsevier.com/content/abstract/scopus_id/85102590202||ISSN:||01782312||DOI:||10.1515/auto-2020-0122|
|Appears in Collections:||Publications of the HSU Researchers|
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checked on May 21, 2022
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