方法证据记录
GRU
The Gated Recurrent Unit (GRU) is a gated recurrent neural network cell introduced by Cho and colleagues in 2014 that captures long-range dependencies in sequential data using update and reset gates, achieving performance comparable to LSTM with fewer parameters.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Gated Recurrent Unit
分类方法记录 · ml-model / deep-learning
- Cho, K. et al. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. EMNLP. · URL
- Chung, J., Gulcehre, C., Cho, K. & Bengio, Y. (2014). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. NIPS 2014 Deep Learning Workshop. arXiv:1412.3555 · URL
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