Representation Learning.

The article discusses representation learning (RL), with a particular focus given to its use within the context of historical practice. Topics mentioned include vectors and vector spaces, artificial neural networks and the relationship between texts and images, full-text search, and optical characte...

Descripción completa

Detalles Bibliográficos
Publicado en:American Historical Review Vol. 128; no. 3; pp. 1350 - 1354
Autor principal: Schmidt, Benjamin
Formato: Artículo
Publicado: Oxford University Press / USA Sep2023
Materias:
Acceso en línea:Ver este registro en EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=172362156&site=ehost-live
header:
  @attributes:
    shortDbName: hlh
    uiTerm: 172362156
    longDbName: Humanities International Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    jinfo:
      jid:
        00028762
        AHS
      jtl: American Historical Review
      issn: 00028762
      maglogo: N
    pubinfo:
      dt: Sep2023
      vid: 128
      iid: 3
      pid: 622
      pub: Oxford University Press / USA
    artinfo:
      ui:
        172362156
        10.1093/ahr/rhad363
      ppf: 1350
      ppct: 4
      formats:
        fmt:
          – @attributes:
              type: T
          – @attributes:
              type: P
              size: 88KB
      tig:
        atl: Representation Learning.
      aug:
        au: Schmidt, Benjamin
        affil: Vice-President of Information Design at Nomic AI, New York, US
      su:
        Machine learning
        Historical research methods
        Vector spaces
        Artificial neural networks
        Keyword searching
        Optical character recognition
      sug:
        subj:
          Machine learning
          Historical research methods
          Vector spaces
          Artificial neural networks
          Keyword searching
          Optical character recognition
      ab: The article discusses representation learning (RL), with a particular focus given to its use within the context of historical practice. Topics mentioned include vectors and vector spaces, artificial neural networks and the relationship between texts and images, full-text search, and optical character recognition (OCR).
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
    refInfo:
    copyright:
      @attributes:
        flag: Y
      custom: © 2019 The American Historical Association.
      item: American Historical Review
      holder: Oxford University Press / USA
      dt:
        @attributes:
          year: 2023
    holdings:
      @attributes:
        islocal: N