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arXiv · 1902.03793

Understanding over-parameterized deep networks by geometrization

Xiao Dong·Ling Zhou
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Abstract

A complete understanding of the widely used over-parameterized deep networks is a key step for AI. In this work we try to give a geometric picture of over-parameterized deep networks using our geometrization scheme. We show that the Riemannian geometry of network complexity plays a key role in understanding the basic properties of over-parameterizaed deep networks, including the generalization, convergence and parameter sensitivity. We also point out deep networks share lots of similarities with quantum computation systems. This can be regarded as a strong support of our proposal that geometrization is not only the bible for physics, it is also the key idea to understand deep learning systems.

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Xiao Dong, Ling Zhou. 2019-02-11. Understanding over-parameterized deep networks by geometrization. https://arxiv.org/abs/1902.03793

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