مقایسهٔ روش‌ها

	شبکه عصبی بازگشتیِ تبیین‌پذیر	حافظه طولانی کوتاه‌مدت (LSTM)
حوزه	یادگیری عمیق	یادگیری عمیق
خانواده	Machine learning	Machine learning
سال پیدایش	2017–2020	1997
پدیدآور	Arrived via XAI literature (Arrieta et al., Lundberg & Lee, and attention-based RNN work)	Hochreiter, S. & Schmidhuber, J.
نوع	Interpretability framework applied to sequence models	Recurrent neural network with gated memory cells
منبع بنیادین	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.
نام‌های دیگر	Explainable RNN, Interpretable RNN, XAI-RNN, Transparent Recurrent Neural Network	LSTM, LSTM network, LSTM-RNN, long short-term memory RNN
مرتبط	5	4
خلاصه	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.