Approximation of BV functions by neural networks: A regularity theory approach.
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| Title: | Approximation of BV functions by neural networks: A regularity theory approach. |
|---|---|
| Authors: | Avelin, Benny1 (AUTHOR) benny.avelin@math.uu.se, Julin, Vesa2 (AUTHOR) vesa.julin@jyu.fi |
| Source: | Analysis & Applications. Oct2025, Vol. 23 Issue 7, p1129-1179. 51p. |
| Database: | Mathematics Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: msf DbLabel: Mathematics Source An: 187438086 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Approximation of BV functions by neural networks: A regularity theory approach. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Avelin%2C+Benny%22">Avelin, Benny</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> benny.avelin@math.uu.se</i><br /><searchLink fieldCode="AR" term="%22Julin%2C+Vesa%22">Julin, Vesa</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> vesa.julin@jyu.fi</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Analysis+%26+Applications%22">Analysis & Applications</searchLink>. Oct2025, Vol. 23 Issue 7, p1129-1179. 51p. |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=msf&AN=187438086 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1142/S0219530525500046 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 51 StartPage: 1129 Titles: – TitleFull: Approximation of BV functions by neural networks: A regularity theory approach. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Avelin, Benny – PersonEntity: Name: NameFull: Julin, Vesa IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 02195305 Numbering: – Type: volume Value: 23 – Type: issue Value: 7 Titles: – TitleFull: Analysis & Applications Type: main |
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