方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 可解释多层感知机× | 可解释 Transformer× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2010s–present | 2017–2021 |
| 提出者≠ | Lundberg & Lee (SHAP); Ribeiro et al. (LIME); broader XAI community | Vaswani et al. (Transformer); explainability extensions by Chefer et al. and the broader XAI community |
| 类型≠ | Supervised feedforward neural network with interpretability layer | Interpretable deep learning model |
| 开创性文献≠ | Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. link ↗ | Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30. link ↗ |
| 别名 | XMLP, Interpretable MLP, Explainable feedforward neural network, Transparent MLP | XAI Transformer, Interpretable Transformer, Transparent Transformer, Explainable Attention Model |
| 相关 | 4 | 4 |
| 摘要≠ | An Explainable Multilayer Perceptron (XMLP) is a standard feedforward neural network trained with backpropagation, augmented with post-hoc interpretability techniques — such as SHAP values, LIME, or integrated gradients — that attribute each prediction to individual input features. The combination retains the MLP's approximation power while satisfying transparency requirements common in regulated or high-stakes domains. | An Explainable Transformer combines a standard or pre-trained Transformer architecture with post-hoc or built-in interpretability techniques — such as attention rollout, gradient-weighted attention, or SHAP — to reveal which input tokens or regions drove each prediction. The approach bridges high predictive accuracy with the transparency required in high-stakes or regulated domains. |
| ScholarGate数据集 ↗ |
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