Krahasoni metodat

	Rrjeti Nervor Rekurent i Shpjegueshëm	Long Short-Term Memory (LSTM)
Fusha	Mësimi i thellë	Mësimi i thellë
Familja	Machine learning	Machine learning
Viti i origjinës	2017–2020	1997
Krijuesi	Arrived via XAI literature (Arrieta et al., Lundberg & Lee, and attention-based RNN work)	Hochreiter, S. & Schmidhuber, J.
Lloji	Interpretability framework applied to sequence models	Recurrent neural network with gated memory cells
Burimi themelues	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.
Emërtime të tjera	Explainable RNN, Interpretable RNN, XAI-RNN, Transparent Recurrent Neural Network	LSTM, LSTM network, LSTM-RNN, long short-term memory RNN
Të lidhura	5	4
Përmbledhja	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.