“Batch normalized recurrent neural networks”这个工作是最早尝试将BN引入RNN的,它构建了5层的RNN和LSTM,它的结论是:水平方向的BN对效果有损害作用,垂直方向BN能够加快参数收敛速度,但是相对基准无BN对照组实验看可能存在过拟合问题。但是这个过拟合是由于训练数据规模不够还是模型造成的并无结论
Laurent, Ce´sar, Pereyra, Gabriel, Brakel, Philemon, Zhang, Ying, and Bengio, Yoshua. Batch nor- malized recurrent neural networks. CoRR, abs/1510.01378, 2015. URL http://arxiv.org/ abs/1510.01378.Laurent, Cesar, et al. "Batch normalized recurrent neural networks." Acoustics, Speech ...
[1] Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. ICML, 2015.[2] Ce ́sar Laurent, Gabriel Pereyra, Phile ́mon Brakel, Ying Zhang, and Yoshua Bengio. Batch normalized recurrent neural networks. arXiv preprint ...
(1) “Batch normalized recurrent neural networks”这个工作是最早尝试将BN引入RNN的,它构建了5层的RNN和LSTM,它的结论是:水平方向的BN对效果有损害作用,垂直方向BN能够加快参数收敛速度,但是相对基准无BN对照组实验看可能存在过拟合问题。但是这个过拟合是由于训练数据规模不够还是模型造成的并无结论。(2) “Deep...
Batch normalized recurrent neural networks. arXiv preprint arXiv:1510.01378, 2015. [3] Tim Cooijmans, Nicolas Ballas, Ce ́sar Laurent, and Aaron Courville. Recurrent batch normalization. arXiv preprint arXiv:1603.09025, 2016. [4] Ba, J. L., Kiros, J. R., and Hinton, G. E. (2016)...
[2] Ce ́sar Laurent, Gabriel Pereyra, Phile ́mon Brakel, Ying Zhang, and Yoshua Bengio. Batch normalized recurrent neural networks. arXiv preprint arXiv:1510.01378, 2015. [3] Tim Cooijmans, Nicolas Ballas, Ce ́sar Laurent, and Aaron Courville. Recurrent batch normalization. arXiv pre...
Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.9% top-5 validation error (and 4.8% test error), exceeding the accuracy of human raters. 摘要 训练深度神经网络的复杂性在于,每层输入的分布在训练过程中会发生变化,因为...
在实际的实验中，效果并不是很好。2015年arXiv上的《Batch Normalized Recurrent Neural Networks》应该算...
“Batch normalized recurrent neural networks”这个工作是最早尝试将BN引入RNN的,它构建了5层的RNN和LSTM,它的结论是:水平方向的BN对效果有损害作用,垂直方向BN能够加快参数收敛速度,但是相对基准无BN对照组实验看可能存在过拟合问题。但是这个过拟合是由于训练数据规模不够还是模型造成的并无结论。
It can be used with most network types, such as Multilayer Perceptrons, Convolutional Neural Networks and Recurrent Neural Networks. Probably Use Before the Activation Batch normalization may be used on the inputs to the layer before or after the activation function in the previous layer. It may...