【論文筆記】A Theoretically Grounded Application of Dropout in Recurrent Neural Networks（2016）
這篇論文從貝葉斯論的角度分析了RNN網絡的優化過程，並以此爲基礎提出了一種對RNN有效的dropout方法。它用到了近似變分推斷的數學方法，這個我啃了好久還是苦於數學能力看得雲裏霧裏。但還是先把筆記記下來吧。 1.介紹與現狀 作者提出，RNN因爲缺少正則化，很容易陷入過擬合。現在也有許多這方面的努力，特別是之前還有人基於實驗結果提出在RNN上運用dropout效果反而會變差這種結論。作者反駁說這些
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相關文章
- 1. 論文筆記：Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
- 2. The Unreasonable Effectiveness of Recurrent Neural Networks
- 3. 論文閱讀：A Critical Review of Recurrent Neural Networks for Sequence Learning
- 4. 論文筆記：ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks
- 5. A Critical Review of Recurrent Neural Networks for Sequence Learning
- 6. Multiresolution Recurrent Neural Networks: An Application to...
- 7. <A Critical Review of Recurrent Neural Networks for Sequence Learning>閱讀筆記
- 8. 【Binarized Neural Networks】論文筆記
- 9. 《C-RNN-GAN: Continuous recurrent neural networks with adversarial training》論文筆記
- 10. 《Improved Recurrent Neural Networks for Session-based Recommendations》 DLRS 2016 閱讀筆記