Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Academic Paper Generation   In this work, we tackle the problem of structured text generation,
specifically academic paper generation in $\LaTeX{}$, inspired by the
surprisingly good results of basic character-level language models. Our
motivation is using more recent and advanced methods of language modeling on a
more complex dataset of $\LaTeX{}$ source files to generate realistic academic
papers. Our first contribution is preparing a dataset with $\LaTeX{}$ source
files on recent open-source computer vision papers. Our second contribution is
experimenting with recent methods of language modeling and text generation such
as Transformer and Transformer-XL to generate consistent $\LaTeX{}$ code. We
report cross-entropy and bits-per-character (BPC) results of the trained
models, and we also discuss interesting points on some examples of the
generated $\LaTeX{}$ code.

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The categories of this paper are: stat
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
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The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
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The categories of this paper are: funct-an