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Explainable Multilayer Perceptron/Evidence
Method evidence record

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.

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Source record

Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.

Explainable Multilayer Perceptron (MLP with Post-hoc Interpretability)
Taxonomic method record · 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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Related methods

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Taxonomic bucketExplainable LSTMmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketExplainable Transformermachine-suggested · Relational suggestion, not evidence.Same method familyMultilayer Perceptronmachine-suggested · Relational suggestion, not evidence.Same method familyRandom Forestmachine-suggested · Relational suggestion, not evidence.

Evidence status

Sources recorded, not reviewed

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Sources

2 recorded citations, copied from the method source record.

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