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| 설명 가능한 LSTM (Explainable LSTM)× | 설명 가능한 순환 신경망× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2017–2019 | 2017–2020 |
| 창시자≠ | Lundberg & Lee (SHAP); Ribeiro et al. (LIME); community synthesis | Arrived via XAI literature (Arrieta et al., Lundberg & Lee, and attention-based RNN work) |
| 유형≠ | Interpretable deep learning (post-hoc explainability) | Interpretability framework applied to sequence models |
| 원전≠ | Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. link ↗ | 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 ↗ |
| 별칭 | XAI-LSTM, interpretable LSTM, LSTM with SHAP, transparent LSTM | Explainable RNN, Interpretable RNN, XAI-RNN, Transparent Recurrent Neural Network |
| 관련 | 5 | 5 |
| 요약≠ | Explainable LSTM pairs a trained Long Short-Term Memory network with post-hoc interpretability techniques — chiefly SHAP, LIME, integrated gradients, or attention visualization — to reveal which time steps, tokens, or features drive each prediction. It bridges the accuracy of recurrent deep learning with the transparency demanded by high-stakes domains such as clinical decision support, fraud detection, and regulatory compliance. | 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. |
| ScholarGate데이터셋 ↗ |
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