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A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks
July 29, 2017 · Declared Dead · 🏛 International Conference on Learning Representations
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Authors
Behnam Neyshabur, Srinadh Bhojanapalli, Nathan Srebro
arXiv ID
1707.09564
Category
cs.LG: Machine Learning
Citations
643
Venue
International Conference on Learning Representations
Last Checked
3 months ago
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.
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