Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Size-Independent Sample Complexity of Neural Networks   We study the sample complexity of learning neural networks, by providing new
bounds on their Rademacher complexity assuming norm constraints on the
parameter matrix of each layer. Compared to previous work, these complexity
bounds have improved dependence on the network depth, and under some additional
assumptions, are fully independent of the network size (both depth and width).
These results are derived using some novel techniques, which may be of
independent interest.

35
The categories of this paper are: mtrl-th
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The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
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The categories of this paper are: q-fin
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
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The categories of this paper are: supr-con
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The categories of this paper are: quant-ph
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The categories of this paper are: funct-an
3
The categories of this paper are: physics
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The categories of this paper are: comp-gas
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The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys