Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| TextCNN× | Dilatoitu CNN× | |
|---|---|---|
| Tieteenala | Syväoppiminen | Syväoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2014 | 2016 |
| Kehittäjä≠ | Kim, Y. | van den Oord, A. et al.; Bai, S., Kolter, J.Z. & Koltun, V. |
| Tyyppi≠ | Convolutional neural network (deep learning) | Deep learning (dilated 1D convolutional network) |
| Alkuperäislähde≠ | Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. EMNLP. DOI ↗ | van den Oord, A. et al. (2016). WaveNet: A Generative Model for Raw Audio. arXiv. link ↗ |
| Rinnakkaisnimet≠ | CNN — Metin Sınıflandırma (TextCNN), convolutional neural network for sentence classification, sentence-level CNN, TextCNN | Dilate Edilmiş CNN (WaveNet / TCN), WaveNet, Temporal Convolutional Network, TCN |
| Liittyvät | 5 | 5 |
| Tiivistelmä≠ | TextCNN is a convolutional neural network for text classification, introduced by Yoon Kim in 2014, that applies parallel convolution filters of different window sizes over word embeddings to capture local n-gram patterns. It is fast and effective for sentiment analysis and topic classification. | 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. |
| ScholarGateAineisto ↗ |
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