长时间学习滞后
Learning Over Long Time Lags
摘要：
递归神经网络（RNN）在学习时间序列数据之间的依赖关系方面的优势使RNN区别于其他深度学习模型。最近，在这个新兴领域提出了许多进展。然而，在文献中缺乏对RNN中记忆模型的全面综述。本文对RNN和长短期记忆（LSTM）模型进行了综述。然后，提供了一个调查的最新进展，在不同的记忆增强和学习技术，以捕捉长期依赖的RNN。
英文摘要：
The advantage of recurrent neural networks (RNNs) in learning dependencies between time-series data has distinguished RNNs from other deep learning models. Recently, many advances are proposed in this emerging field. However, there is a lack of comprehensive review on memory models in RNNs in the literature. This paper provides a fundamental review on RNNs and long short term memory (LSTM) model. Then, provides a surveys of recent advances in different memory enhancements and learning techniques for capturing long term dependencies in RNNs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
