Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Novel Document Generation Process for Topic Detection based on
  Hierarchical Latent Tree Models   We propose a novel document generation process based on hierarchical latent
tree models (HLTMs) learned from data. An HLTM has a layer of observed word
variables at the bottom and multiple layers of latent variables on top. For
each document, we first sample values for the latent variables layer by layer
via logic sampling, then draw relative frequencies for the words conditioned on
the values of the latent variables, and finally generate words for the document
using the relative word frequencies. The motivation for the work is to take
word counts into consideration with HLTMs. In comparison with LDA-based
hierarchical document generation processes, the new process achieves
drastically better model fit with much fewer parameters. It also yields more
meaningful topics and topic hierarchies. It is the new state-of-the-art for the
hierarchical topic detection.

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The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th