Papers › PAC-Bayesian Margin Bounds for Convolutional Neural Networks
PAC-Bayesian Margin Bounds for Convolutional Neural Networks
Konstantinos Pitas, Mike Davies, Pierre Vandergheynst
Recently the generalization error of deep neural networks has been analyzed through the PAC-Bayesian framework, for the case of fully connected layers. We adapt this approach to the convolutional setting.
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