Title: | Expressing uncertainty in neural networks for production systems | Authors: | Multaheb, Samim Ahmad Zimmering, Bernd Niggemann, Oliver |
Language: | eng | 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 | ISSN: | 0178-2312 2196-677X |
Publisher DOI: | 10.1515/auto-2020-0122 |
Appears in Collections: | 3 - Publication references (without fulltext) |
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