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Computer Science > Machine Learning

arXiv:1901.02354 (cs)
[Submitted on 6 Jan 2019 (v1), last revised 13 Jan 2019 (this version, v2)]

Title:Geometrization of deep networks for the interpretability of deep learning systems

Authors:Xiao Dong, Ling Zhou
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Abstract:How to understand deep learning systems remains an open problem. In this paper we propose that the answer may lie in the geometrization of deep networks. Geometrization is a bridge to connect physics, geometry, deep network and quantum computation and this may result in a new scheme to reveal the rule of the physical world. By comparing the geometry of image matching and deep networks, we show that geometrization of deep networks can be used to understand existing deep learning systems and it may also help to solve the interpretability problem of deep learning systems.
Comments: 9 pages, draft version
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
Cite as: arXiv:1901.02354 [cs.LG]
  (or arXiv:1901.02354v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.1901.02354

Submission history

From: Xiao Dong [view email]
[v1] Sun, 6 Jan 2019 14:32:45 UTC (24 KB)
[v2] Sun, 13 Jan 2019 17:20:05 UTC (25 KB)
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