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 encountered when training traditional recurrent networks. LSTM incorporates memory cells and gating mechanisms that regulate the flow of information, allowing it to effectively learn and remember long-range dependencies in sequential data. This advancement has significantly improved the performance of sequence modeling tasks, enabling networks to capture intricate patterns across extensive data sequences.