Porovnať metódy
Prezrite si vybrané metódy vedľa seba; riadky, ktoré sa líšia, sú zvýraznené.
| Dilatovaná konvolučná neurónová sieť (Dilated CNN)× | Náhodný les× | Model sekvencia-na-sekvenciu× | |
|---|---|---|---|
| Odbor≠ | Hlboké učenie | Strojové učenie | Hlboké učenie |
| Rodina | Machine learning | Machine learning | Machine learning |
| Rok vzniku≠ | 2016 | 2001 | 2014 |
| Tvorca≠ | van den Oord, A. et al.; Bai, S., Kolter, J.Z. & Koltun, V. | Breiman, L. | Sutskever, I.; Cho, K. |
| Typ≠ | Deep learning (dilated 1D convolutional network) | Ensemble (bagging of decision trees) | Encoder-decoder neural network (deep learning) |
| Pôvodný zdroj≠ | van den Oord, A. et al. (2016). WaveNet: A Generative Model for Raw Audio. arXiv. link ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ | Sutskever, I., Vinyals, O. & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks. NeurIPS. link ↗ |
| Ďalšie názvy≠ | Dilate Edilmiş CNN (WaveNet / TCN), WaveNet, Temporal Convolutional Network, TCN | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble | Dizi-Dizi Modeli (Seq2Seq — Encoder-Decoder), encoder-decoder model, seq2seq, sequence to sequence learning |
| Príbuzné≠ | 5 | 4 | 5 |
| Zhrnutie≠ | 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. | Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree. | 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. |
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