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- URN to cite this document:
- urn:nbn:de:bvb:355-epub-578918
- DOI to cite this document:
- 10.5283/epub.57891
Abstract
Real-world data typically exhibit aleatoric uncertainty which has to be considered during data-driven decision-making to assess the confidence of the decision provided by machine learning models. To propagate aleatoric uncertainty repre-sented by probability distributions (PDs) through neural net-works (NNs), both sampling-based and function approxima-tion-based methods have been proposed. ...
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