Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Dкновеirziena atkārtojošais neironu tīkls× | Iegādīts rekurents vienums (GRU)× | |
|---|---|---|
| Nozare | Dziļā mācīšanās | Dziļā mācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1997 | 2014 |
| Autors≠ | Schuster, M. & Paliwal, K.K. | Cho, K. et al. |
| Tips≠ | Recurrent neural network (sequence model) | Gated recurrent neural network unit |
| Pirmavots≠ | Schuster, M. & Paliwal, K.K. (1997). Bidirectional Recurrent Neural Networks. IEEE Transactions on Signal Processing, 45(11), 2673–2681. DOI ↗ | Cho, K. et al. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. EMNLP. link ↗ |
| Citi nosaukumi≠ | Çift Yönlü RNN / BiLSTM / BiGRU, bidirectional recurrent neural network, BiLSTM, BiGRU | Kapılı Tekrarlayan Birim (GRU), gated recurrent unit, gated recurrent network |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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. | 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. |
| ScholarGateDatu kopa ↗ |
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