方法对比
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| 可解释门控循环单元 (Explainable GRU)× | 可解释长短期记忆网络× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2014 (GRU); 2016–2017 (XAI integration) | 2017–2019 |
| 提出者≠ | Cho, K. et al. (GRU); explainability layer via Lundberg & Lee (SHAP) and Ribeiro et al. (LIME) | Lundberg & Lee (SHAP); Ribeiro et al. (LIME); community synthesis |
| 类型≠ | Recurrent neural network with post-hoc or attention-based interpretability | Interpretable deep learning (post-hoc explainability) |
| 开创性文献≠ | Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. Proceedings of EMNLP 2014, 1724–1734. DOI ↗ | Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. link ↗ |
| 别名 | XAI-GRU, Interpretable GRU, GRU with explainability, Transparent GRU | XAI-LSTM, interpretable LSTM, LSTM with SHAP, transparent LSTM |
| 相关 | 5 | 5 |
| 摘要≠ | Explainable GRU pairs the Gated Recurrent Unit, a compact and efficient recurrent architecture, with explainability techniques such as SHAP, LIME, or attention weighting to reveal which time steps and features drove each prediction. It brings interpretability to sequential modelling without sacrificing the GRU's ability to capture temporal dependencies. | 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. |
| ScholarGate数据集 ↗ |
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