Σύγκριση μεθόδων

	Επεξηγήσιμο Επαναλαμβανόμενο Νευρωνικό Δίκτυο	Αναδρομικό Νευρωνικό Δίκτυο
Πεδίο	Βαθιά Μάθηση	Βαθιά Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2017–2020	1986–1990
Δημιουργός	Arrived via XAI literature (Arrieta et al., Lundberg & Lee, and attention-based RNN work)	Rumelhart, D. E.; Elman, J. L.
Τύπος	Interpretability framework applied to sequence models	Sequential neural network
Θεμελιώδης πηγή	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.	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
Εναλλακτικές ονομασίες	Explainable RNN, Interpretable RNN, XAI-RNN, Transparent Recurrent Neural Network	RNN, Elman network, Jordan network, simple recurrent network
Συναφείς	5	3
Σύνοψη	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.	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.