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Machine learningDeep learning / NLP / CV

Transformer Zinazoeleka

Transformer Zinazoeleka huunganisha usanifu wa kawaida au uliopewa mafunzo awali wa Transformer na mbinu za utendakazi wa baada ya uchanganuzi au zilizo ndani — kama vile utoaji wa umakini (attention rollout), umakini wenye uzito wa mteremko (gradient-weighted attention), au SHAP — ili kufichua ni tokeni au sehemu zipi za pembejeo zilizoathiri kila utabiri. Mbinu hii huunganisha usahihi wa juu wa utabiri na uwazi unaohitajika katika nyanja zenye dhamana kubwa au zilizo na udhibiti.

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Vyanzo

  1. 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. link
  2. Chefer, H., Gur, S., & Wolf, L. (2021). Transformer interpretability beyond attention visualization. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 782–791. DOI: 10.1109/CVPR46437.2021.00084

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Explainable Transformer (Interpretability-Augmented Transformer Model). ScholarGate. https://scholargate.app/sw/deep-learning/explainable-transformer

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Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.

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Imerejelewa na

ScholarGateExplainable Transformer (Explainable Transformer (Interpretability-Augmented Transformer Model)). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/deep-learning/explainable-transformer · Seti ya data: https://doi.org/10.5281/zenodo.20539026