Incorporating Domain Knowledge into Deep Neural Networks
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.
Cite
@article{arxiv.2103.00180,
title = {Incorporating Domain Knowledge into Deep Neural Networks},
author = {Tirtharaj Dash and Sharad Chitlangia and Aditya Ahuja and Ashwin Srinivasan},
journal= {arXiv preprint arXiv:2103.00180},
year = {2021}
}
Comments
Submitted to IJCAI-2021 Survey Track (6+2 pages)