Long Short-Term Memory

This paper introduces Long Short-Term Memory (LSTM), a type of recurrent neural network architecture designed to address the vanishing gradient problem in traditional RNNs. LSTM networks use special memory cells and gating mechanisms to selectively remember or forget information over long sequences, enabling them to learn long-term dependencies in data. The authors demonstrate LSTM's superior performance in various sequence learning tasks compared to other RNN architectures.