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KENN: Enhancing Deep Neural Networks by Leveraging Knowledge for Time Series Forecasting
End-to-end data-driven machine learning methods often have exuberant requirements in terms of quality and quantity of training data which are often impractical to fulfill in real-world applications.
A Survey on Knowledge integration techniques with Artificial Neural Networks for seq-2-seq/time series models
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.
KINN: Incorporating Expert Knowledge in Neural Networks
The promise of ANNs to automatically discover and extract useful features/patterns from data without dwelling on domain expertise although seems highly promising but comes at the cost of high reliance on large amount of accurately labeled data, which is often hard to acquire and formulate especially in time-series domains like anomaly detection, natural disaster management, predictive maintenance and healthcare.
