Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Затворен рекурентен модул (GRU)× | Двупосочна рекурентна невронна мрежа (Bidirectional RNN)× | |
|---|---|---|
| Област | Дълбоко обучение | Дълбоко обучение |
| Семейство | Machine learning | Machine learning |
| Година на възникване≠ | 2014 | 1997 |
| Създател≠ | Cho, K. et al. | Schuster, M. & Paliwal, K.K. |
| Тип≠ | Gated recurrent neural network unit | Recurrent neural network (sequence model) |
| Основополагащ източник≠ | Cho, K. et al. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. EMNLP. link ↗ | Schuster, M. & Paliwal, K.K. (1997). Bidirectional Recurrent Neural Networks. IEEE Transactions on Signal Processing, 45(11), 2673–2681. DOI ↗ |
| Други названия≠ | Kapılı Tekrarlayan Birim (GRU), gated recurrent unit, gated recurrent network | Çift Yönlü RNN / BiLSTM / BiGRU, bidirectional recurrent neural network, BiLSTM, BiGRU |
| Свързани | 5 | 5 |
| Резюме≠ | 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. | 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. |
| ScholarGateНабор от данни ↗ |
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