手法を比較

	説明可能なリカレントニューラルネットワーク	Long Short-Term Memory (LSTM)
分野	深層学習	深層学習
系統	Machine learning	Machine learning
提唱年	2017–2020	1997
提唱者	Arrived via XAI literature (Arrieta et al., Lundberg & Lee, and attention-based RNN work)	Hochreiter, S. & Schmidhuber, J.
種類	Interpretability framework applied to sequence models	Recurrent neural network with gated memory cells
原典	Arrieta, A. B., Diaz-Rodriguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115.	Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
別名	Explainable RNN, Interpretable RNN, XAI-RNN, Transparent Recurrent Neural Network	LSTM, LSTM network, LSTM-RNN, long short-term memory RNN
関連	5	4
概要	An Explainable Recurrent Neural Network (XAI-RNN) pairs a standard RNN architecture with a post-hoc or intrinsic interpretability method — such as SHAP, LIME, integrated gradients, or attention visualization — to reveal which input time steps or tokens most influence the model's sequential predictions, without sacrificing predictive accuracy.	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.