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

15 Feb 2018 (modified: 23 Feb 2018)ICLR 2018 Conference Blind SubmissionReaders: Everyone
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
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