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[10-b]Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1):1929–1958, 2014. [11] Li Wan, Matthew Zeiler, Sixin Zhang, Yann LeCun, ...
这是一些对于论文《Dropout: A Simple Way to Prevent Neural Networks from Overfitting》的简单的读后总结,首先先奉上该文章的下载超链接:Dropout 这篇文章是由多伦多计算机科学大学学系(Department of Computer Science, University of Toronto)的人员合作完成的,作者分别是Nitish Srivastava、Geoffrey Hinton、Alex Kriz...
model=NeuralNetwork()rdrop=RDropout(drop_probability=0.5)criterion=nn.CrossEntropyLoss()optimizer=torch.optim.SGD(model.parameters(),lr=0.01)forepochinrange(num_epochs):forimages,labelsindataloader:optimizer.zero_grad()outputs=model(images)outputs=rdrop(outputs)loss=criterion(outputs,labels)loss.backwa...
在2012年,Hinton在其论文《Improving neural networks by preventing co-adaptation of feature detectors》中提出Dropout。当一个复杂的前馈神经网络被训练在小的数据集时,容易造成过拟合。为了防止过拟合,可以通过阻止特征检测器的共同作用来提高神经网络的性能。
We show that dropout in neural networks (NNs) can be interpreted as a Bayesian approximation. As a direct result we obtain tools for modelling uncertainty with dropout NNs -- extracting information from existing models that has been thrown away so far. This mitigates the problem of representing ...
【论文:解释RNN中Dropout有效性的(近似贝叶斯推理)理论框架】《A Theoretically Grounded Application of Dropout in Recurrent Neural Networks》Y Gal [University of Cambridge] (2015) http://t.cn/R4LltU3
Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural ...
Deep learning:四十一(Dropout简单理解)实验中nn.dropoutFraction和深度学习(二十二)Dropout浅层理解与实现实验中的level是指该神经元被dropout(即:丢弃)的概率,而论文“Dropout: A simple way to prevent neural networks from overfitting”中的概率p是指神经元被present(即:被选中不被dropout)的概率。即:p=1 - dr...