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The advantage of recurrent neural networks (RNNs) in learning dependenciesbetween time-series data has distinguished RNNs from other deep learningmodels. Recently, many advances are proposed in this emerging field. However,there is a lack of comprehensive review on memory models in RNNs in theliterature. This paper provides a fundamental review on RNNs and long shortterm memory (LSTM) model. Then, provides a surveys of recent advances indifferent memory enhancements and learning techniques for capturing long termdependencies in RNNs.

Learning Over Long Time Lags