Fast and reliable uncertainty quantification with neural network ensembles for industrial image classification.
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| Title: | Fast and reliable uncertainty quantification with neural network ensembles for industrial image classification. |
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| Authors: | Thuy, Arthur1,2, arthur.thuy@ugent.be, Benoit, Dries F.1,2, dries.benoit@ugent.be |
| Source: | Annals of Operations Research; Oct2025, Vol. 353 Issue 2, p517-543, 27p |
| Database: | Applied Science & Technology Source |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: aci DbLabel: Applied Science & Technology Source An: 188685372 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Fast and reliable uncertainty quantification with neural network ensembles for industrial image classification. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Thuy%2C+Arthur%22">Thuy, Arthur</searchLink><relatesTo>1,2</relatesTo>, <i>arthur.thuy@ugent.be</i><br /><searchLink fieldCode="AU" term="%22Benoit%2C+Dries+F%2E%22">Benoit, Dries F.</searchLink><relatesTo>1,2</relatesTo>, <i>dries.benoit@ugent.be</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Annals+of+Operations+Research%22">Annals of Operations Research</searchLink>; Oct2025, Vol. 353 Issue 2, p517-543, 27p |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=188685372 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10479-024-06440-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 27 StartPage: 517 Titles: – TitleFull: Fast and reliable uncertainty quantification with neural network ensembles for industrial image classification. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Thuy, Arthur – PersonEntity: Name: NameFull: Benoit, Dries F. IsPartOfRelationships: – BibEntity: Dates: – D: 11 M: 10 Text: Oct2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 02545330 Numbering: – Type: volume Value: 353 – Type: issue Value: 2 Titles: – TitleFull: Annals of Operations Research Type: main |
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