Salīdzināt metodes

	Paskaidrojamais LSTM	Skaidrojams GRU
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2017–2019	2014 (GRU); 2016–2017 (XAI integration)
Autors	Lundberg & Lee (SHAP); Ribeiro et al. (LIME); community synthesis	Cho, K. et al. (GRU); explainability layer via Lundberg & Lee (SHAP) and Ribeiro et al. (LIME)
Tips	Interpretable deep learning (post-hoc explainability)	Recurrent neural network with post-hoc or attention-based interpretability
Pirmavots	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.	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.
Citi nosaukumi	XAI-LSTM, interpretable LSTM, LSTM with SHAP, transparent LSTM	XAI-GRU, Interpretable GRU, GRU with explainability, Transparent GRU
Saistītās	5	5
Kopsavilkums	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.	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.