Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fast Gradient-Based Inference with Continuous Latent Variable Models in
  Auxiliary Form   We propose a technique for increasing the efficiency of gradient-based
inference and learning in Bayesian networks with multiple layers of continuous
latent vari- ables. We show that, in many cases, it is possible to express such
models in an auxiliary form, where continuous latent variables are
conditionally deterministic given their parents and a set of independent
auxiliary variables. Variables of mod- els in this auxiliary form have much
larger Markov blankets, leading to significant speedups in gradient-based
inference, e.g. rapid mixing Hybrid Monte Carlo and efficient gradient-based
optimization. The relative efficiency is confirmed in ex- periments.

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The categories of this paper are: atom-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: physics
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The categories of this paper are: comp-gas
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The categories of this paper are: eess
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-th
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The categories of this paper are: ao-sci
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The categories of this paper are: q-bio
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The categories of this paper are: solv-int
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The categories of this paper are: chao-dyn
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The categories of this paper are: astro-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: plasm-ph