Very Deep Convolutional Networks for Text Classification
實現：https://github.com/zonetrooper32/VDCNN Abstract NLP的許多任務的方法是循環神經網絡，特別是LSTM，與深層的卷積神經網絡相比，這些網絡都比較淺，所以我們構建VDCNN，這是字符級的，並且僅用小的卷積和pooling操作，用了29層卷積層。 1 Introduction NLP的任務是處理文本，提取文本，採用不同形式表示相同的信息，我們希望把標
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相關文章
- 1. (35) [arXiv17] Very Deep Convolutional Networks for Text Classification
- 2. Very Deep Convolutional Networks for Text Classification之每日一篇
- 3. 《Character-level Convolutional Networks for Text Classification》
- 4. Graph Convolutional Networks for Text Classification
- 5. Recurrent Convolutional Neural Networks for Text Classification
- 6. Very Deep Convolutional Networks for Large-Scale Image Recognition
- 7. VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
- 8. VGG: VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
- 9. VGG--VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
- 10. VGG——Very deep convolutional networks for large-scale image recognition