Network-size independent covering number bounds for deep networks.

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arXiv. 2017 Nov 02;:arXiv:1711.00753

Network-size independent covering number bounds for deep networks. Branson Lab

Kabra M, Branson KM

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Abstract

We give a covering number bound for deep learning networks that is independent of the size of the network. The key for the simple analysis is that for linear classifiers, rotating the data doesn't affect the covering number. Thus, we can ignore the rotation part of each layer's linear transformation, and get the covering number bound by concentrating on the scaling part.

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Network-size independent covering number bounds for deep networks.

Abstract

Janelia Authors