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

TL;DR

This paper introduces a PAC-Bayesian framework to derive spectral norm-based margin bounds for neural networks, providing theoretical insights into their generalization capabilities.

Contribution

It presents a novel PAC-Bayesian approach to establish generalization bounds using spectral and Frobenius norms of neural network weights.

Findings

Derived a new generalization bound for neural networks

Bound relates spectral norms and Frobenius norms of weights

Provides theoretical understanding of neural network generalization

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.