Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Topic Modeling in Embedding Spaces   Topic modeling analyzes documents to learn meaningful patterns of words.
However, existing topic models fail to learn interpretable topics when working
with large and heavy-tailed vocabularies. To this end, we develop the Embedded
Topic Model (ETM), a generative model of documents that marries traditional
topic models with word embeddings. In particular, it models each word with a
categorical distribution whose natural parameter is the inner product between a
word embedding and an embedding of its assigned topic. To fit the ETM, we
develop an efficient amortized variational inference algorithm. The ETM
discovers interpretable topics even with large vocabularies that include rare
words and stop words. It outperforms existing document models, such as latent
Dirichlet allocation (LDA), in terms of both topic quality and predictive
performance.

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The categories of this paper are: chao-dyn
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The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
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The categories of this paper are: hep-th
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The categories of this paper are: math-ph
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The categories of this paper are: supr-con
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The categories of this paper are: hep-lat
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The categories of this paper are: math
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The categories of this paper are: econ
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The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci