Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning for Symbolic Mathematics   Neural networks have a reputation for being better at solving statistical or
approximate problems than at performing calculations or working with symbolic
data. In this paper, we show that they can be surprisingly good at more
elaborated tasks in mathematics, such as symbolic integration and solving
differential equations. We propose a syntax for representing mathematical
problems, and methods for generating large datasets that can be used to train
sequence-to-sequence models. We achieve results that outperform commercial
Computer Algebra Systems such as Matlab or Mathematica.

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The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
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The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
16
The categories of this paper are: stat
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: eess
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-lat
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The categories of this paper are: patt-sol
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The categories of this paper are: astro-ph