Long Short-Term Memory

A type of recurrent neural network architecture specifically designed to capture long-range dependencies in sequential data, using a system of gates to selectively remember or forget information over time. LSTMs addressed the vanishing gradient problem that limited earlier RNNs and were the dominant architecture for sequence modeling tasks before transformers took over.