Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| CNN dilatată× | Unitate Recurentă Gated (GRU)× | |
|---|---|---|
| Domeniu | Învățare profundă | Învățare profundă |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 2016 | 2014 |
| Autorul original≠ | van den Oord, A. et al.; Bai, S., Kolter, J.Z. & Koltun, V. | Cho, K. et al. |
| Tip≠ | Deep learning (dilated 1D convolutional network) | Gated recurrent neural network unit |
| Sursa seminală≠ | van den Oord, A. et al. (2016). WaveNet: A Generative Model for Raw Audio. arXiv. link ↗ | Cho, K. et al. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. EMNLP. link ↗ |
| Denumiri alternative≠ | Dilate Edilmiş CNN (WaveNet / TCN), WaveNet, Temporal Convolutional Network, TCN | Kapılı Tekrarlayan Birim (GRU), gated recurrent unit, gated recurrent network |
| Înrudite | 5 | 5 |
| Rezumat≠ | A Dilated CNN is a one-dimensional convolutional network whose receptive field grows exponentially with depth, letting it model long-range structure in time series and audio signals. WaveNet (van den Oord et al., 2016) and the Temporal Convolutional Network of Bai, Kolter and Koltun (2018) are the prominent members of this family. | 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. |
| ScholarGateSet de date ↗ |
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