Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| TextCNN× | Двунаправленная РНС× | Сверточная нейронная сеть с дилатацией× | |
|---|---|---|---|
| Область | Глубокое обучение | Глубокое обучение | Глубокое обучение |
| Семейство | Machine learning | Machine learning | Machine learning |
| Год появления≠ | 2014 | 1997 | 2016 |
| Автор метода≠ | Kim, Y. | Schuster, M. & Paliwal, K.K. | van den Oord, A. et al.; Bai, S., Kolter, J.Z. & Koltun, V. |
| Тип≠ | Convolutional neural network (deep learning) | Recurrent neural network (sequence model) | Deep learning (dilated 1D convolutional network) |
| Основополагающий источник≠ | Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. EMNLP. DOI ↗ | Schuster, M. & Paliwal, K.K. (1997). Bidirectional Recurrent Neural Networks. IEEE Transactions on Signal Processing, 45(11), 2673–2681. DOI ↗ | van den Oord, A. et al. (2016). WaveNet: A Generative Model for Raw Audio. arXiv. link ↗ |
| Другие названия≠ | CNN — Metin Sınıflandırma (TextCNN), convolutional neural network for sentence classification, sentence-level CNN, TextCNN | Çift Yönlü RNN / BiLSTM / BiGRU, bidirectional recurrent neural network, BiLSTM, BiGRU | Dilate Edilmiş CNN (WaveNet / TCN), WaveNet, Temporal Convolutional Network, TCN |
| Связанные | 5 | 5 | 5 |
| Сводка≠ | 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 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. | 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. |
| ScholarGateНабор данных ↗ |
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