Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On the Convergence of Stochastic Variational Inference in Bayesian
  Networks   We highlight a pitfall when applying stochastic variational inference to
general Bayesian networks. For global random variables approximated by an
exponential family distribution, natural gradient steps, commonly starting from
a unit length step size, are averaged to convergence. This useful insight into
the scaling of initial step sizes is lost when the approximation factorizes
across a general Bayesian network, and care must be taken to ensure practical
convergence. We experimentally investigate how much of the baby (well-scaled
steps) is thrown out with the bath water (exact gradients).

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The categories of this paper are: acc-phys
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
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The categories of this paper are: solv-int
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The categories of this paper are: patt-sol
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The categories of this paper are: quant-ph
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The categories of this paper are: q-fin
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The categories of this paper are: math-ph
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The categories of this paper are: hep-th
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The categories of this paper are: nucl-th
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The categories of this paper are: nlin
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The categories of this paper are: adap-org