Dropout: A Simple Way to Prevent Neural Networks from Over tting 论文阅读

时间 2021-01-02

标签 dropout 繁體版

原文原文链接

Dropout: A Simple Way to Prevent Neural Networks from Over tting 基本原理结果敏感性分析对特征图谱的影响：可以看到使用dropout后cnn计算出来的特征图谱要平滑的很多稀疏性的影响可以看出，使用droupout后很多隐层单元的activation 激活值都接近与0，只有少部分的神经元的激活值特别大。 p的影响左图表

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1. 【论文精读】Dropout: A Simple Way to Prevent Neural Networks from Overfitting
2. Dropout:A Simple Way to Prevent Neural Networks from Overfitting
3. 译：《Dropout: A Simple Way to Prevent Neural Networks from Overﬁtting》
4. (10) [JMLR14] Dropout: A Simple Way to Prevent Neural Networks from Overfitting
5. 论文笔记2——Dropout：A simple way to prevent neural networks from overfitting
6. 【论文快读】DeepFool: a simple and accurate method to fool deep neural networks(2016)
7. 深度学习论文阅读：Convolutional Neural Networks for Sentence Classification （TextCNN）
8. 论文阅读：《Convolutional Neural Networks for Sentence Classification》
9. 论文阅读：Aggregated Residual Transformations for Deep Neural Networks（ResNeXt）
10. 文献阅读 - Binarized Neural Networks
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1. 【论文精读】Dropout: A Simple Way to Prevent Neural Networks from Overfitting
2. Dropout:A Simple Way to Prevent Neural Networks from Overfitting
3. 译：《Dropout: A Simple Way to Prevent Neural Networks from Overﬁtting》
4. (10) [JMLR14] Dropout: A Simple Way to Prevent Neural Networks from Overfitting
5. 论文笔记2——Dropout：A simple way to prevent neural networks from overfitting
6. 【论文快读】DeepFool: a simple and accurate method to fool deep neural networks(2016)
7. 深度学习论文阅读：Convolutional Neural Networks for Sentence Classification （TextCNN）
8. 论文阅读：《Convolutional Neural Networks for Sentence Classification》
9. 论文阅读：Aggregated Residual Transformations for Deep Neural Networks（ResNeXt）
10. 文献阅读 - Binarized Neural Networks

>>更多相关文章<<