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Computer Science > Machine Learning

arXiv:1902.03793 (cs)
[Submitted on 11 Feb 2019]

Title:Understanding over-parameterized deep networks by geometrization

Authors: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.
Comments: 6 pages
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:1902.03793 [cs.LG]
  (or arXiv:1902.03793v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.1902.03793

Submission history

From: Xiao Dong [view email]
[v1] Mon, 11 Feb 2019 09:53:13 UTC (18 KB)
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