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A review of some techniques for inclusion of domain-knowledge into deep neural networks

Scientific reports, 2022-01, Vol.12 (1), p.1040-1040, Article 1040 [Peer Reviewed Journal]

2022. The Author(s). ;The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2022 ;ISSN: 2045-2322 ;EISSN: 2045-2322 ;DOI: 10.1038/s41598-021-04590-0 ;PMID: 35058487

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  • Title:
    A review of some techniques for inclusion of domain-knowledge into deep neural networks
  • Author: Dash, Tirtharaj ; Chitlangia, Sharad ; Ahuja, Aditya ; Srinivasan, Ashwin
  • Subjects: Neural networks
  • Is Part Of: Scientific reports, 2022-01, Vol.12 (1), p.1040-1040, Article 1040
  • Description: We present a survey of ways in which existing scientific knowledge are included when constructing models with neural networks. The inclusion of domain-knowledge is of special interest not just to constructing scientific assistants, but also, many other areas that involve understanding data using human-machine collaboration. In many such instances, machine-based model construction may benefit significantly from being provided with human-knowledge of the domain encoded in a sufficiently precise form. This paper examines the inclusion of domain-knowledge by means of changes to: the input, the loss-function, and the architecture of deep networks. The categorisation is for ease of exposition: in practice we expect a combination of such changes will be employed. In each category, we describe techniques that have been shown to yield significant changes in the performance of deep neural networks.
  • Publisher: England: Nature Publishing Group
  • Language: English
  • Identifier: ISSN: 2045-2322
    EISSN: 2045-2322
    DOI: 10.1038/s41598-021-04590-0
    PMID: 35058487
  • Source: PubMed Central
    Coronavirus Research Database
    ProQuest Central
    DOAJ Directory of Open Access Journals
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