Long Short-Term Memory

This paper introduces the Long Short-Term Memory (LSTM) architecture, a type of recurrent neural network designed to address the vanishing gradient problem in standard RNNs. LSTM networks are capable of learning long-term dependencies in sequential data by using special memory cells and gates to regulate the flow of information, allowing them to selectively remember or forget information over extended periods. This makes LSTMs particularly well-suited for tasks involving time series prediction, speech recognition, and other applications where long-range dependencies are crucial.