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| Erklärbares Rekurrentes Neuronales Netz× | Long Short-Term Memory (LSTM)× | |
|---|---|---|
| Fachgebiet | Deep Learning | Deep Learning |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2017–2020 | 1997 |
| Urheber≠ | Arrived via XAI literature (Arrieta et al., Lundberg & Lee, and attention-based RNN work) | Hochreiter, S. & Schmidhuber, J. |
| Typ≠ | Interpretability framework applied to sequence models | Recurrent neural network with gated memory cells |
| Wegweisende Quelle≠ | 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. DOI ↗ | Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. DOI ↗ |
| Aliasnamen | Explainable RNN, Interpretable RNN, XAI-RNN, Transparent Recurrent Neural Network | LSTM, LSTM network, LSTM-RNN, long short-term memory RNN |
| Verwandt≠ | 5 | 4 |
| Zusammenfassung≠ | 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. |
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