The Growing Cost of Deep Learning for Source Code: Attempting to mitigate problems associated with the trend toward massive dataset scaling.

The article opines on attempts to mitigate the problems engendered by massive dataset scaling with a focus on the costs of deep learning for source code. Costs associated with training artificial intelligences and artificial neural networks on a given computer model are identified. The author calls...

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Detalles Bibliográficos
Publicado en:Communications of the ACM Vol. 65; no. 1; pp. 31 - 34
Autores principales: Hellendoorn, Vincent J., Sawant, Anand Ashok
Formato: Artículo
Publicado: Association for Computing Machinery Jan2022
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        atl: The Growing Cost of Deep Learning for Source Code: Attempting to mitigate problems associated with the trend toward massive dataset scaling.
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          Hellendoorn, Vincent J.
          Sawant, Anand Ashok
        affil:
          Assistant professor of computer science at Carnegie Mellon University, Pittsburgh, PA, USA
          Research Professional at Siemens Corporate Technology, Princeton, NJ, USA
      su:
        Source code
        Deep learning
        Artificial intelligence
        Computer simulation
        Computer workstation clusters
        Big data
      sug:
        subj:
          Source code
          Deep learning
          Artificial intelligence
          Computer simulation
          Computer workstation clusters
          Big data
      ab: The article opines on attempts to mitigate the problems engendered by massive dataset scaling with a focus on the costs of deep learning for source code. Costs associated with training artificial intelligences and artificial neural networks on a given computer model are identified. The author calls networking computing clusters to combine computing power, increase collaboration, and reduce costs.
      pubtype: Periodical
      doctype: Article
      src: R
    language: English
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