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Training Affective Computer Vision Models by Crowdsourcing Soft-Target Labels.
Washington, Peter; Kalantarian, Haik; Kent, Jack; Husic, Arman; Kline, Aaron; Leblanc, Emilie; Hou, Cathy; Mutlu, Cezmi; Dunlap, Kaitlyn; Penev, Yordan; Stockham, Nate; Chrisman, Brianna; Paskov, Kelley; Jung, Jae-Yoon; Voss, Catalin; Haber, Nick; Wall, Dennis P.
Afiliación
  • Washington P; Department of Bioengineering, Stanford University.
  • Kalantarian H; Department of Pediatrics (Systems Medicine), Stanford University.
  • Kent J; Department of Pediatrics (Systems Medicine), Stanford University.
  • Husic A; Department of Pediatrics (Systems Medicine), Stanford University.
  • Kline A; Department of Pediatrics (Systems Medicine), Stanford University.
  • Leblanc E; Department of Pediatrics (Systems Medicine), Stanford University.
  • Hou C; Department of Computer Science, Stanford University.
  • Mutlu C; Department of Electrical Engineering, Stanford University.
  • Dunlap K; Department of Pediatrics (Systems Medicine).
  • Penev Y; Department of Pediatrics (Systems Medicine), Stanford University.
  • Stockham N; Department of Neuroscience, Stanford University.
  • Chrisman B; Department of Bioengineering, Stanford University.
  • Paskov K; Department of Biomedical Data Science, Stanford University.
  • Jung JY; Department of Pediatrics (Systems Medicine), Stanford University.
  • Voss C; Department of Computer Science, Stanford University.
  • Haber N; Graduate School of Education, Stanford University.
  • Wall DP; Departments of Pediatrics (Systems Medicine), Biomedical Data Science, and Psychiatry and Behavioral Sciences, Stanford University.
Cognit Comput ; 13(5): 1363-1373, 2021 Sep.
Article en En | MEDLINE | ID: mdl-35669554
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Cognit Comput Año: 2021 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Cognit Comput Año: 2021 Tipo del documento: Article Pais de publicación: Estados Unidos