Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generating Symbolic Reasoning Problems with Transformer GANs   Constructing training data for symbolic reasoning domains is challenging:
Existing instances are typically hand-crafted and too few to be trained on
directly and synthetically generated instances are often hard to evaluate in
terms of their meaningfulness. We study the capabilities of GANs and
Wasserstein GANs equipped with Transformer encoders to generate sensible and
challenging training data for symbolic reasoning domains. We conduct
experiments on two problem domains where Transformers have been successfully
applied recently: symbolic mathematics and temporal specifications in
verification. Even without autoregression, our GAN models produce syntactically
correct instances. We show that the generated data can be used as a substitute
for real training data when training a classifier, and, especially, that
training data can be generated from a real dataset that is too small to be
trained on directly. Using a GAN setting also allows us to alter the target
distribution: We show that by adding a classifier uncertainty part to the
generator objective, we obtain a dataset that is even harder to solve for a
classifier than our original dataset.

18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
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The categories of this paper are: nucl-th
16
The categories of this paper are: stat
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The categories of this paper are: hep-lat
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The categories of this paper are: solv-int
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The categories of this paper are: ao-sci
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The categories of this paper are: q-fin