Dropout: A Simple Way to Prevent Neural Networks from Over tting 論文閱讀
Dropout: A Simple Way to Prevent Neural Networks from Over tting 基本原理 結果 敏感性分析 對特徵圖譜的影響： 可以看到 使用dropout後cnn計算出來的特徵圖譜要平滑的很多 稀疏性的影響 可以看出，使用droupout後 很多隱層單元的activation 激活值都接近與0，只有少部分的神經元的激活值特別大。 p的影響 左圖表
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- 3. 譯：《Dropout: A Simple Way to Prevent Neural Networks from Overfitting》
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相關文章
- 1. 【論文精讀】Dropout: A Simple Way to Prevent Neural Networks from Overfitting
- 2. Dropout:A Simple Way to Prevent Neural Networks from Overfitting
- 3. 譯：《Dropout: A Simple Way to Prevent Neural Networks from Overfitting》
- 4. (10) [JMLR14] Dropout: A Simple Way to Prevent Neural Networks from Overfitting
- 5. 論文筆記2——Dropout：A simple way to prevent neural networks from overfitting
- 6. 【論文快讀】DeepFool: a simple and accurate method to fool deep neural networks(2016)
- 7. 深度學習論文閱讀：Convolutional Neural Networks for Sentence Classification （TextCNN）
- 8. 論文閱讀：《Convolutional Neural Networks for Sentence Classification》
- 9. 論文閱讀：Aggregated Residual Transformations for Deep Neural Networks（ResNeXt）
- 10. 文獻閱讀 - Binarized Neural Networks