手法を比較

	Long Short-Term Memory (LSTM)	リカレントニューラルネットワーク (RNN)
分野	深層学習	深層学習
系統	Machine learning	Machine learning
提唱年	1997	1986–1990
提唱者	Hochreiter, S. & Schmidhuber, J.	Rumelhart, D. E.; Elman, J. L.
種類	Recurrent neural network with gated memory cells	Sequential neural network
原典	Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
別名	LSTM, LSTM network, LSTM-RNN, long short-term memory RNN	RNN, Elman network, Jordan network, simple recurrent network
関連	4	3
概要	Long Short-Term Memory (LSTM) is a gated recurrent neural network architecture introduced by Hochreiter and Schmidhuber in 1997. It was designed to learn dependencies across long sequences by using dedicated memory cells and three learned gates — forget, input, and output — that control what information is retained, updated, or passed forward at each time step.	A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models.