विधियों की तुलना करें

	व्याख्या योग्य आवर्ती तंत्रिका नेटवर्क	पुनरावर्ती तंत्रिका नेटवर्क
क्षेत्र	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2017–2020	1986–1990
प्रवर्तक	Arrived via XAI literature (Arrieta et al., Lundberg & Lee, and attention-based RNN work)	Rumelhart, D. E.; Elman, J. L.
प्रकार	Interpretability framework applied to sequence models	Sequential neural network
मौलिक स्रोत	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.
उपनाम	Explainable RNN, Interpretable RNN, XAI-RNN, Transparent Recurrent Neural Network	RNN, Elman network, Jordan network, simple recurrent network
संबंधित	5	3
सारांश	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.