【论文笔记】A Theoretically Grounded Application of Dropout in Recurrent Neural Networks（2016）
这篇论文从贝叶斯论的角度分析了RNN网络的优化过程，并以此为基础提出了一种对RNN有效的dropout方法。它用到了近似变分推断的数学方法，这个我啃了好久还是苦于数学能力看得云里雾里。但还是先把笔记记下来吧。 1.介绍与现状 作者提出，RNN因为缺少正则化，很容易陷入过拟合。现在也有许多这方面的努力，特别是之前还有人基于实验结果提出在RNN上运用dropout效果反而会变差这种结论。作者反驳说这些
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相关文章
- 1. 论文笔记：Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
- 2. The Unreasonable Effectiveness of Recurrent Neural Networks
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- 4. 论文笔记：ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks
- 5. A Critical Review of Recurrent Neural Networks for Sequence Learning
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- 7. <A Critical Review of Recurrent Neural Networks for Sequence Learning>阅读笔记
- 8. 【Binarized Neural Networks】论文笔记
- 9. 《C-RNN-GAN: Continuous recurrent neural networks with adversarial training》论文笔记
- 10. 《Improved Recurrent Neural Networks for Session-based Recommendations》 DLRS 2016 阅读笔记