论文标题
SEQ-2-SEQ/时间序列模型的知识整合技术的调查
A Survey on Knowledge integration techniques with Artificial Neural Networks for seq-2-seq/time series models
论文作者
论文摘要
近年来，随着庞大的计算能力的出现和大量数据的可用性，深度神经网络已使探索几个领域的未知区域。但是有时，由于数据不足，数据质量差，可能无法广泛涵盖该领域的数据，因此它们的表现不佳。基于知识的系统利用专家知识来做出决策并适当采取行动。此类系统在决策过程中保留可解释性。本文着重于探索技术，以将专业知识与深度神经网络相结合，以序列到序列和时间序列模型，以提高其性能和解释性。
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.
