Confronta i metodi
Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| CNN Dilatata× | Modello Sequence-to-Sequence× | |
|---|---|---|
| Campo | Apprendimento profondo | Apprendimento profondo |
| Famiglia | Machine learning | Machine learning |
| Anno di origine≠ | 2016 | 2014 |
| Ideatore≠ | van den Oord, A. et al.; Bai, S., Kolter, J.Z. & Koltun, V. | Sutskever, I.; Cho, K. |
| Tipo≠ | Deep learning (dilated 1D convolutional network) | Encoder-decoder neural network (deep learning) |
| Fonte seminale≠ | van den Oord, A. et al. (2016). WaveNet: A Generative Model for Raw Audio. arXiv. link ↗ | Sutskever, I., Vinyals, O. & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks. NeurIPS. link ↗ |
| Alias≠ | Dilate Edilmiş CNN (WaveNet / TCN), WaveNet, Temporal Convolutional Network, TCN | Dizi-Dizi Modeli (Seq2Seq — Encoder-Decoder), encoder-decoder model, seq2seq, sequence to sequence learning |
| Correlati | 5 | 5 |
| Sintesi≠ | 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 sequence-to-sequence (Seq2Seq) model, introduced by Sutskever, Vinyals and Le and by Cho and colleagues in 2014, is an encoder-decoder neural network that maps a variable-length input sequence to a variable-length output sequence. It is the foundation of machine translation, text summarization, dialogue systems and code generation. |
| ScholarGateInsieme di dati ↗ |
|
|