Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to superoptimize programs - Workshop Version   Superoptimization requires the estimation of the best program for a given
computational task. In order to deal with large programs, superoptimization
techniques perform a stochastic search. This involves proposing a modification
of the current program, which is accepted or rejected based on the improvement
achieved. The state of the art method uses uniform proposal distributions,
which fails to exploit the problem structure to the fullest. To alleviate this
deficiency, we learn a proposal distribution over possible modifications using
Reinforcement Learning. We provide convincing results on the superoptimization
of "Hacker's Delight" programs.

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The categories of this paper are: plasm-ph
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The categories of this paper are: chem-ph
36
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: supr-con
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: atom-ph
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The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc