Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Understanding Architectures Learnt by Cell-based Neural Architecture
  Search   Neural architecture search (NAS) searches architectures automatically for
given tasks, e.g., image classification and language modeling. Improving the
search efficiency and effectiveness have attracted increasing attention in
recent years. However, few efforts have been devoted to understanding the
generated architectures. In this paper, we first reveal that existing NAS
algorithms (e.g., DARTS, ENAS) tend to favor architectures with wide and
shallow cell structures. These favorable architectures consistently achieve
fast convergence and are consequently selected by NAS algorithms. Our empirical
and theoretical study further confirms that their fast convergence derives from
their smooth loss landscape and accurate gradient information. Nonetheless,
these architectures may not necessarily lead to better generalization
performance compared with other candidate architectures in the same search
space, and therefore further improvement is possible by revising existing NAS
algorithms.

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The categories of this paper are: quant-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: physics
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The categories of this paper are: q-bio
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The categories of this paper are: hep-ex
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The categories of this paper are: math
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The categories of this paper are: nucl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: acc-phys
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The categories of this paper are: q-fin
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The categories of this paper are: adap-org
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The categories of this paper are: dg-ga
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The categories of this paper are: cond-mat
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The categories of this paper are: solv-int
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The categories of this paper are: hep-ph