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Deep Network Approximation in Terms of Intrinsic Parameters. (arXiv:2111.07964v2 [cs.LG] UPDATED)
Web: http://arxiv.org/abs/2111.07964
June 16, 2022, 1:11 a.m. | Zuowei Shen, Haizhao Yang, Shijun Zhang
cs.LG updates on arXiv.org arxiv.org
One of the arguments to explain the success of deep learning is the powerful
approximation capacity of deep neural networks. Such capacity is generally
accompanied by the explosive growth of the number of parameters, which, in
turn, leads to high computational costs. It is of great interest to ask whether
we can achieve successful deep learning with a small number of learnable
parameters adapting to the target function. From an approximation perspective,
this paper shows that the number of parameters …
More from arxiv.org / cs.LG updates on arXiv.org
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