On the Difficulty of Epistemic Uncertainty Quantification in Machine Learning: The Case of Direct Uncertainty Estimation through Loss Minimisation. | D2R Server publishing the DBLP Bibliography Database, hosted at L3S Research Center

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dc:creator	<https://dblp.l3s.de/d2r/resource/authors/Viktor_Bengs>
dc:creator	<https://dblp.l3s.de/d2r/resource/authors/Willem_Waegeman>
foaf:homepage	<http://dx.doi.org/doi.org%2F10.48550%2FarXiv.2203.06102>
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dc:identifier	DBLP journals/corr/abs-2203-06102 (xsd:string)
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rdfs:label	On the Difficulty of Epistemic Uncertainty Quantification in Machine Learning: The Case of Direct Uncertainty Estimation through Loss Minimisation. (xsd:string)
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dc:title	On the Difficulty of Epistemic Uncertainty Quantification in Machine Learning: The Case of Direct Uncertainty Estimation through Loss Minimisation. (xsd:string)
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