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[論文筆記] [2010] Understanding the Difficulty of Training Deep Feedforward Neural Networks
時間 2020-12-24
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深度學習
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機器學習
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這篇論文主要是從參數初始化和激活函數的角度,通過實驗中觀察網絡在訓練迭代時每層的 activations 和 gradients,來探究訓練深層模型困難的原因,並提出了一種新的參數初始化方式來加快模型訓練時的收斂。 Effect of Activation Functions and Saturation During Training sigmoid 激活函數在之前已經被證明會減慢學習的速度,如
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相關文章
1.
Xavier——Understanding the difficulty of training deep feedforward neural networks
2.
【Deep Learning】筆記:Understanding the difficulty of training deep feedforward neural networks
3.
論文解析-《Understanding the difficulty of training deep feedforward neural networks》
4.
Paper之DL之BP:《Understanding the difficulty of training deep feedforward neural networks》
5.
Understanding Neural Networks Through Deep Visualization 論文筆記
6.
CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes2018—論文筆記
7.
【論文筆記】Training Very Deep Networks - Highway Networks
8.
Machine Learning & Deep Learning 論文閱讀筆記
9.
神經網絡不同激活函數比較--讀《Understanding the difficulty of training deep feedforward neural networks》
10.
論文閱讀筆記(五十三):Understanding Deep Convolutional Networks
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