方法证据记录
Explainable Multilayer Perceptron
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.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Explainable Multilayer Perceptron (MLP with Post-hoc Interpretability)
分类方法记录 · ml-model / deep-learning
- Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. · URL
- Explainable artificial intelligence. Wikipedia. · URL
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