Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Understanding the Role of Over-Parametrization in Generalization
  of Neural Networks   Despite existing work on ensuring generalization of neural networks in terms
of scale sensitive complexity measures, such as norms, margin and sharpness,
these complexity measures do not offer an explanation of why neural networks
generalize better with over-parametrization. In this work we suggest a novel
complexity measure based on unit-wise capacities resulting in a tighter
generalization bound for two layer ReLU networks. Our capacity bound correlates
with the behavior of test error with increasing network sizes, and could
potentially explain the improvement in generalization with
over-parametrization. We further present a matching lower bound for the
Rademacher complexity that improves over previous capacity lower bounds for
neural networks.

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The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
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The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
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The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
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The categories of this paper are: comp-gas
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The categories of this paper are: funct-an
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The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex
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The categories of this paper are: q-fin
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The categories of this paper are: supr-con
18
The categories of this paper are: econ