Salīdzināt metodes

	Skaidrojams GRU	Paskaidrojamais LSTM
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2014 (GRU); 2016–2017 (XAI integration)	2017–2019
Autors	Cho, K. et al. (GRU); explainability layer via Lundberg & Lee (SHAP) and Ribeiro et al. (LIME)	Lundberg & Lee (SHAP); Ribeiro et al. (LIME); community synthesis
Tips	Recurrent neural network with post-hoc or attention-based interpretability	Interpretable deep learning (post-hoc explainability)
Pirmavots	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.	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.
Citi nosaukumi	XAI-GRU, Interpretable GRU, GRU with explainability, Transparent GRU	XAI-LSTM, interpretable LSTM, LSTM with SHAP, transparent LSTM
Saistītās	5	5
Kopsavilkums	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.	Explainable LSTM pairs a trained Long Short-Term Memory network with post-hoc interpretability techniques — chiefly SHAP, LIME, integrated gradients, or attention visualization — to reveal which time steps, tokens, or features drive each prediction. It bridges the accuracy of recurrent deep learning with the transparency demanded by high-stakes domains such as clinical decision support, fraud detection, and regulatory compliance.