方法对比
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| 可解释循环神经网络× | 循环神经网络× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2017–2020 | 1986–1990 |
| 提出者≠ | Arrived via XAI literature (Arrieta et al., Lundberg & Lee, and attention-based RNN work) | Rumelhart, D. E.; Elman, J. L. |
| 类型≠ | Interpretability framework applied to sequence models | Sequential neural network |
| 开创性文献≠ | 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 ↗ | Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. DOI ↗ |
| 别名 | Explainable RNN, Interpretable RNN, XAI-RNN, Transparent Recurrent Neural Network | RNN, Elman network, Jordan network, simple recurrent network |
| 相关≠ | 5 | 3 |
| 摘要≠ | 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. |
| ScholarGate数据集 ↗ |
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