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| Gated Recurrent Unit (GRU)× | Attention Mechanism× | Bidirectional RNN× | |
|---|---|---|---|
| Fachgebiet | Deep Learning | Deep Learning | Deep Learning |
| Familie | Machine learning | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2014 | 2015 | 1997 |
| Urheber≠ | Cho, K. et al. | Bahdanau, D.; Luong, M.T. | Schuster, M. & Paliwal, K.K. |
| Typ≠ | Gated recurrent neural network unit | Neural attention layer (encoder-decoder) | Recurrent neural network (sequence model) |
| Wegweisende Quelle≠ | Cho, K. et al. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. EMNLP. link ↗ | Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. ICLR. link ↗ | Schuster, M. & Paliwal, K.K. (1997). Bidirectional Recurrent Neural Networks. IEEE Transactions on Signal Processing, 45(11), 2673–2681. DOI ↗ |
| Aliasnamen≠ | Kapılı Tekrarlayan Birim (GRU), gated recurrent unit, gated recurrent network | Dikkat Mekanizması (Bahdanau / Luong Attention), dikkat mekanizmasi, neural attention, additive attention | Çift Yönlü RNN / BiLSTM / BiGRU, bidirectional recurrent neural network, BiLSTM, BiGRU |
| Verwandt | 5 | 5 | 5 |
| Zusammenfassung≠ | 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. | The attention mechanism, introduced by Bahdanau, Cho and Bengio in 2015 and refined by Luong, Pham and Manning the same year, lets a sequence decoder dynamically learn which of the encoder's outputs to focus on at each step. Before the Transformer, it substantially improved machine-translation quality by freeing models from compressing an entire input into a single fixed vector. | A Bidirectional RNN, introduced by Schuster and Paliwal in 1997, processes a sequence in both forward and backward directions so that every position has access to its full surrounding context. With LSTM or GRU cells (BiLSTM/BiGRU) it is the standard approach for named-entity recognition, sequence labelling, and speech recognition. |
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