পদ্ধতির তুলনা করুন

	ব্যাখ্যাযোগ্য বহুস্তর পারসেপ্ট্রন	ব্যাখ্যাযোগ্য ট্রান্সফরমার (Explainable Transformer)
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2010s–present	2017–2021
প্রবর্তক	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
ধরন	Supervised feedforward neural network with interpretability layer	Interpretable deep learning model
মৌলিক উৎস	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.
অপর নাম	XMLP, Interpretable MLP, Explainable feedforward neural network, Transparent MLP	XAI Transformer, Interpretable Transformer, Transparent Transformer, Explainable Attention Model
সম্পর্কিত	4	4
সারসংক্ষেপ	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.