PAC-Bayesian Margin Bounds for Convolutional Neural Networks

30 Dec 2017 · 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.

PDF Abstract

Code

Add Remove Mark official

konstantinos-p/PAC_Bayesian_General…

Tasks

Add Remove

Datasets

Add Datasets introduced or used in this paper

Results from the Paper

Edit

Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods

Add Remove

No methods listed for this paper. Add relevant methods here

Edit Social Preview

PAC-Bayesian Margin Bounds for Convolutional Neural Networks

Code Edit Add Remove Mark official

Tasks Edit Add Remove

Datasets Edit

Results from the Paper Edit

Methods Edit Add Remove

Code

Add Remove Mark official

Tasks

Add Remove

Datasets

Results from the Paper

Edit

Methods

Add Remove