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.

Submitted to IJCAI-2021 Survey Track (6+2 pages)

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• Superpixel-based Knowledge Infusion in Deep Neural Networks for Image Classification
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• Framework for Learning and Control in the Classical and Quantum Domains
• Explanatory Debiasing: Involving Domain Experts in the Data Generation Process to Mitigate Representation Bias in AI Systems
• Identifying type II quasars at intermediate redshift with few-shot learning photometric classification
• Beyond Traditional Neural Networks: Toward adding Reasoning and Learning Capabilities through Computational Logic Techniques
• NuGraph2 with Context-Aware Inputs: Physics-Inspired Improvements in Semantic Segmentation
• Domain Knowledge Based Temporal-Spatial Graph Convolution Network for ECG Recognition