(35) [arXiv17] Very Deep Convolutional Networks for Text Classification
計劃完成深度學習入門的126篇論文第三十五篇，Yann Le Cun等發表的文本分類的論文。 Abstract 許多NLP任務的主要方法是遞歸神經網絡，特別是LSTMs和卷積神經網絡。然而，與推進計算機視覺發展的深度卷積網絡相比，這些架構顯得至關膚淺。咱們提出了一種新的文本處理體系結構(VDCNN)，它直接在字符級操做，只使用小的卷積和池操做。咱們可以代表，該模型的性能隨着深度的增長而增長：使用多
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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
- 3. Very Deep Convolutional Networks for Text Classification之每日一篇
- 4. 《Character-level Convolutional Networks for Text Classification》
- 5. Graph Convolutional Networks for Text Classification
- 6. Recurrent Convolutional Neural Networks for Text Classification
- 7. Very Deep Convolutional Networks for Large-Scale Image Recognition
- 8. 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