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arXiv · 2306.01992

On Size-Independent Sample Complexity of ReLU Networks

Mark Sellke
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Abstract

We study the sample complexity of learning ReLU neural networks from the point of view of generalization. Given norm constraints on the weight matrices, a common approach is to estimate the Rademacher complexity of the associated function class. Previously Golowich-Rakhlin-Shamir (2020) obtained a bound independent of the network size (scaling with a product of Frobenius norms) except for a factor of the square-root depth. We give a refinement which often has no explicit depth-dependence at all.

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Mark Sellke. 2023-06-03. On Size-Independent Sample Complexity of ReLU Networks. https://arxiv.org/abs/2306.01992

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