A Survey on Knowledge integration techniques with Artificial Neural Networks for seq-2-seq/time series models

TL;DR

This survey reviews methods for integrating expert knowledge into deep neural networks, specifically for sequence-to-sequence and time series models, aiming to enhance their performance and interpretability.

Contribution

It provides a comprehensive overview of existing techniques for knowledge integration in neural networks tailored for sequence and time series tasks.

Findings

Knowledge integration improves model interpretability.

Enhanced performance in sequence-to-sequence models.

Better handling of limited or poor-quality data.

Abstract

In recent years, with the advent of massive computational power and the availability of huge amounts of data, Deep neural networks have enabled the exploration of uncharted areas in several domains. But at times, they under-perform due to insufficient data, poor data quality, data that might not be covering the domain broadly, etc. Knowledge-based systems leverage expert knowledge for making decisions and suitably take actions. Such systems retain interpretability in the decision-making process. This paper focuses on exploring techniques to integrate expert knowledge to the Deep Neural Networks for sequence-to-sequence and time series models to improve their performance and interpretability.