विधियों की तुलना करें

	Explainable Multilayer Perceptron	व्याख्यायोग्य ट्रांसफार्मर
क्षेत्र	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2010s–present	2017–2021
प्रवर्तक	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
प्रकार	Supervised feedforward neural network with interpretability layer	Interpretable deep learning model
मौलिक स्रोत	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.	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.
उपनाम	XMLP, Interpretable MLP, Explainable feedforward neural network, Transparent MLP	XAI Transformer, Interpretable Transformer, Transparent Transformer, Explainable Attention Model
संबंधित	4	4
सारांश	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.