Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Query Training: Learning a Worse Model to Infer Better Marginals in
  Undirected Graphical Models with Hidden Variables   Probabilistic graphical models (PGMs) provide a compact representation of
knowledge that can be queried in a flexible way: after learning the parameters
of a graphical model once, new probabilistic queries can be answered at test
time without retraining. However, when using undirected PGMS with hidden
variables, two sources of error typically compound in all but the simplest
models (a) learning error (both computing the partition function and
integrating out the hidden variables is intractable); and (b) prediction error
(exact inference is also intractable). Here we introduce query training (QT), a
mechanism to learn a PGM that is optimized for the approximate inference
algorithm that will be paired with it. The resulting PGM is a worse model of
the data (as measured by the likelihood), but it is tuned to produce better
marginals for a given inference algorithm. Unlike prior works, our approach
preserves the querying flexibility of the original PGM: at test time, we can
estimate the marginal of any variable given any partial evidence. We
demonstrate experimentally that QT can be used to learn a challenging
8-connected grid Markov random field with hidden variables and that it
consistently outperforms the state-of-the-art AdVIL when tested on three
undirected models across multiple datasets.

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The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
2
The categories of this paper are: math
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
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The categories of this paper are: mtrl-th
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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: adap-org
11
The categories of this paper are: hep-lat