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The Ethereal
Quantified advantage of discontinuous weight selection in approximations with deep neural networks
May 03, 2017 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
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Authors
Dmitry Yarotsky
arXiv ID
1705.01365
Category
cs.NE: Neural & Evolutionary
Citations
11
Venue
arXiv.org
Last Checked
4 months ago
Abstract
We consider approximations of 1D Lipschitz functions by deep ReLU networks of a fixed width. We prove that without the assumption of continuous weight selection the uniform approximation error is lower than with this assumption at least by a factor logarithmic in the size of the network.
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