השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| LSTM מוסבר× | GRU מוסבר× | |
|---|---|---|
| תחום | למידה עמוקה | למידה עמוקה |
| משפחה | Machine learning | Machine learning |
| שנת המקור≠ | 2017–2019 | 2014 (GRU); 2016–2017 (XAI integration) |
| הוגה השיטה≠ | 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) |
| סוג≠ | Interpretable deep learning (post-hoc explainability) | Recurrent neural network with post-hoc or attention-based interpretability |
| מקור מכונן≠ | Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. link ↗ | 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. DOI ↗ |
| כינויים | XAI-LSTM, interpretable LSTM, LSTM with SHAP, transparent LSTM | XAI-GRU, Interpretable GRU, GRU with explainability, Transparent GRU |
| קשורות | 5 | 5 |
| תקציר≠ | 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. |
| ScholarGateמערך נתונים ↗ |
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