Сравнение на методи

	Обясним GRU	Вентилна рекурентна единица (GRU)
Област	Дълбоко обучение	Дълбоко обучение
Семейство	Machine learning	Machine learning
Година на възникване	2014 (GRU); 2016–2017 (XAI integration)	2014
Създател	Cho, K. et al. (GRU); explainability layer via Lundberg & Lee (SHAP) and Ribeiro et al. (LIME)	Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y.
Тип	Recurrent neural network with post-hoc or attention-based interpretability	Recurrent neural network with gating
Основополагащ източник	Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. Proceedings of EMNLP 2014, 1724–1734.	Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. In Proceedings of EMNLP 2014, pp. 1724–1734.
Други названия	XAI-GRU, Interpretable GRU, GRU with explainability, Transparent GRU	GRU, GRU network, gated RNN, GRU cell
Свързани	5	3
Резюме	Explainable GRU pairs the Gated Recurrent Unit, a compact and efficient recurrent architecture, with explainability techniques such as SHAP, LIME, or attention weighting to reveal which time steps and features drove each prediction. It brings interpretability to sequential modelling without sacrificing the GRU's ability to capture temporal dependencies.	The Gated Recurrent Unit (GRU), introduced by Cho et al. in 2014, is a streamlined recurrent neural network that uses two learned gates — an update gate and a reset gate — to selectively retain or discard information across time steps, enabling effective sequence modelling with fewer parameters than LSTM.