Salīdzināt metodes

	Skaidrojams daudzslāņu perceptrons	Skaidrojams Transformeris
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2010s–present	2017–2021
Autors	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
Tips	Supervised feedforward neural network with interpretability layer	Interpretable deep learning model
Pirmavots	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.
Citi nosaukumi	XMLP, Interpretable MLP, Explainable feedforward neural network, Transparent MLP	XAI Transformer, Interpretable Transformer, Transparent Transformer, Explainable Attention Model
Saistītās	4	4
Kopsavilkums	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.