Incorporating Domain Knowledge into Deep Neural Networks

• URL: http://arxiv.org/abs/2103.00180v1
• Date: Sat, 27 Feb 2021 10:39:43 GMT
• Title: Incorporating Domain Knowledge into Deep Neural Networks
• Authors: Tirtharaj Dash, Sharad Chitlangia, Aditya Ahuja, Ashwin Srinivasan
• Abstract summary: The inclusion of domain-knowledge is of special interest not just to constructing scientific assistants, but also to many other areas that involve understanding data using human-machine collaboration. This paper examines two broad approaches to encode such knowledge--as logical and numerical constraints--and describes techniques and results obtained in several sub-categories under each of these approaches.
• Score: 2.2186394337073527
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a survey of ways in which domain-knowledge has been 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 two broad approaches to encode such knowledge--as logical and numerical constraints--and describes techniques and results obtained in several sub-categories under each of these approaches.

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