Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Mutation is all you need   Neural architecture search (NAS) promises to make deep learning accessible to
non-experts by automating architecture engineering of deep neural networks.
BANANAS is one state-of-the-art NAS method that is embedded within the Bayesian
optimization framework. Recent experimental findings have demonstrated the
strong performance of BANANAS on the NAS-Bench-101 benchmark being determined
by its path encoding and not its choice of surrogate model. We present
experimental results suggesting that the performance of BANANAS on the
NAS-Bench-301 benchmark is determined by its acquisition function optimizer,
which minimally mutates the incumbent.

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The categories of this paper are: nucl-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
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The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
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The categories of this paper are: cond-mat
3
The categories of this paper are: physics
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
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The categories of this paper are: q-fin
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The categories of this paper are: stat