A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks

Anonymous

Nov 03, 2017 (modified: Nov 03, 2017) ICLR 2018 Conference Blind Submission readers: everyone Show Bibtex
  • Abstract: We present a generalization bound for feedforward neural networks in terms of the product of the spectral norm of the layers and the Frobenius norm of the weights. The generalization bound is derived using a PAC-Bayes analysis.
  • Keywords: Neural Networks, Generalization, PAC-Bayes, Sharpness

Loading