Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Can Shallow Neural Networks Beat the Curse of Dimensionality? A mean
  field training perspective   We prove that the gradient descent training of a two-layer neural network on
empirical or population risk may not decrease population risk at an order
faster than $t^{-4/(d-2)}$ under mean field scaling. Thus gradient descent
training for fitting reasonably smooth, but truly high-dimensional data may be
subject to the curse of dimensionality. We present numerical evidence that
gradient descent training with general Lipschitz target functions becomes
slower and slower as the dimension increases, but converges at approximately
the same rate in all dimensions when the target function lies in the natural
function space for two-layer ReLU networks.

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The categories of this paper are: q-bio
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The categories of this paper are: acc-phys
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
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The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-fin