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(11)[ICML15] Batch Norm: Accelerating Deep Network Training by Reducing Internal Covariate Shift
時間 2020-01-31
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計劃完成深度學習入門的126篇論文第十一篇,Google的Ioffe和Szegedy使用一種新的regularization方法或者說是新的initialization的方法Batch Normalization。 ABSTRACT&INTRODUCTION 摘要 因爲訓練過程當中各層輸入的分佈隨前一層參數的變化而變化,使得深度神經網絡的訓練變得複雜。這須要較低的學習率和謹慎的參數初始化,從而下降
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相關文章
1.
(11)[ICML15] Batch Norm: Accelerating Deep Network Training by Reducing Internal Covariate Shift
2.
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift 論文解讀
3.
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
4.
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift 論文翻譯(轉)
5.
《Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift》論文筆記
6.
【inv2】Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
7.
論文閱讀 Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
8.
論文筆記:Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
9.
[論文閱讀] Batch Normalization: Accelerating Deep Network Training By Reducing Internal Covariate Shift
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