LSTM

1997년 도입 · 논문 5,448편에서 사용

An LSTM is a type of recurrent neural network that addresses the vanishing gradient problem in vanilla RNNs through additional cells, input and output gates. Intuitively, vanishing gradients are solved through additional *additive* components, and forget gate activations, that allow the gradients to flow through the network without vanishing as quickly. (Image Source here) (Introduced by Hochreiter and Schmidhuber)