Gated Recurrent Unit (GRU)
Gated Recurrent Unit (GRU), introduceret af Cho et al. i 2014, er et strømlinet rekurrent neuralt netværk, der anvender to indlærte gates — en opdaterings-gate og en nulstillings-gate — til selektivt at bevare eller kassere information over tidsskridt, hvilket muliggør effektiv sekvensmodellering med færre parametre end LSTM.
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Kilder
- 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. link ↗
- Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. NIPS 2014 Deep Learning Workshop. link ↗
Sådan citerer du denne side
ScholarGate. (2026, June 3). Gated Recurrent Unit (GRU). ScholarGate. https://scholargate.app/da/deep-learning/gated-recurrent-unit
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