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Enhancing Monte Carlo Dropout performance for uncertainty quantification.

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Title: Enhancing Monte Carlo Dropout performance for uncertainty quantification.
Authors: Asgharnezhad, Hamzeh1, h.asgharnezhad@deakin.edu.au, Shamsi, Afshar2, Alizadehsani, Roohallah1,3, Mohammadi, Arash2, Alinejad-Rokny, Hamid4
Source: Neural Computing & Applications; Jun2026, Vol. 38 Issue 11, p1-19, 19p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 194242735
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>; Jun2026, Vol. 38 Issue 11, p1-19, 19p
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        Value: 10.1007/s00521-026-12165-1
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      – Code: eng
        Text: English
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              Text: Jun2026
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              Y: 2026
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