A Review of Some Techniques for Inclusion of Domain-Knowledge into Deep Neural Networks
Abstract
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.
Keywords
Cite
@article{arxiv.2107.10295,
title = {A Review of Some Techniques for Inclusion of Domain-Knowledge into Deep Neural Networks},
author = {Tirtharaj Dash and Sharad Chitlangia and Aditya Ahuja and Ashwin Srinivasan},
journal= {arXiv preprint arXiv:2107.10295},
year = {2022}
}
Comments
16 pages; Accepted at Nature Scientific Reports. arXiv admin note: substantial text overlap with arXiv:2103.00180