(35) [arXiv17] Very Deep Convolutional Networks for Text Classification
计划完成深度学习入门的126篇论文第三十五篇，Yann Le Cun等发表的文本分类的论文。 Abstract 许多NLP任务的主要方法是递归神经网络，特别是LSTMs和卷积神经网络。然而，与推动计算机视觉发展的深度卷积网络相比，这些架构显得相当肤浅。我们提出了一种新的文本处理体系结构(VDCNN)，它直接在字符级操作，只使用小的卷积和池操作。我们能够表明，该模型的性能随着深度的增加而增加：使用多
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相关文章
- 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