Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Joint Learning Approach for Semi-supervised Neural Topic Modeling   Topic models are some of the most popular ways to represent textual data in
an interpret-able manner. Recently, advances in deep generative models,
specifically auto-encoding variational Bayes (AEVB), have led to the
introduction of unsupervised neural topic models, which leverage deep
generative models as opposed to traditional statistics-based topic models. We
extend upon these neural topic models by introducing the Label-Indexed Neural
Topic Model (LI-NTM), which is, to the extent of our knowledge, the first
effective upstream semi-supervised neural topic model. We find that LI-NTM
outperforms existing neural topic models in document reconstruction benchmarks,
with the most notable results in low labeled data regimes and for data-sets
with informative labels; furthermore, our jointly learned classifier
outperforms baseline classifiers in ablation studies.

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The categories of this paper are: hep-ex
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The categories of this paper are: patt-sol
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The categories of this paper are: acc-phys
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The categories of this paper are: funct-an
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The categories of this paper are: comp-gas
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The categories of this paper are: math-ph
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The categories of this paper are: econ
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The categories of this paper are: physics
10
The categories of this paper are: q-bio
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: math
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The categories of this paper are: quant-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: nucl-th
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The categories of this paper are: dg-ga