Machine learningDeep learning / NLP / CV
Gated Recurrent Unit (GRU)
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.
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Sources
- 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 ↗
Related methods
Referenced by
Domain-adaptive GRUExplainable GRUExplainable Recurrent Neural NetworkFine-Tuned GRUFine-Tuned Recurrent Neural NetworkLong Short-Term MemoryMultilingual GRUMultilingual Recurrent Neural NetworkMultimodal GRUMultimodal LSTMMultimodal Recurrent Neural NetworkRecurrent Neural NetworkRoBERTa-based ClassificationSelf-supervised GRUSemi-supervised GRUTransfer Learning with LSTMTransfer Learning with Recurrent Neural NetworkWeakly Supervised GRUWeakly supervised recurrent neural network