A Survey on Knowledge integration techniques with Artificial Neural Networks for seq-2-seq/time series models
This is an incremental survey paper that addresses performance and interpretability problems in deep learning for researchers and practitioners working with sequence-to-sequence and time series models.
This paper surveys techniques for integrating expert knowledge into deep neural networks for sequence-to-sequence and time series models to address performance issues caused by insufficient or poor-quality data, aiming to improve both performance and interpretability.
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.