So sánh phương pháp

	Mạng Perceptron Đa Lớp Giải Thích Được	Explainable LSTM
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2010s–present	2017–2019
Người khởi xướng	Lundberg & Lee (SHAP); Ribeiro et al. (LIME); broader XAI community	Lundberg & Lee (SHAP); Ribeiro et al. (LIME); community synthesis
Loại	Supervised feedforward neural network with interpretability layer	Interpretable deep learning (post-hoc explainability)
Công trình gốc	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.
Tên gọi khác	XMLP, Interpretable MLP, Explainable feedforward neural network, Transparent MLP	XAI-LSTM, interpretable LSTM, LSTM with SHAP, transparent LSTM
Liên quan	4	5
Tóm tắt	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.	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.