Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Random Sum-Product Forests with Residual Links   Tractable yet expressive density estimators are a key building block of
probabilistic machine learning. While sum-product networks (SPNs) offer
attractive inference capabilities, obtaining structures large enough to fit
complex, high-dimensional data has proven challenging. In this paper, we
present random sum-product forests (RSPFs), an ensemble approach for mixing
multiple randomly generated SPNs. We also introduce residual links, which
reference specialized substructures of other component SPNs in order to
leverage the context-specific knowledge encoded within them. Our empirical
evidence demonstrates that RSPFs provide better performance than their
individual components. Adding residual links improves the models further,
allowing the resulting ResSPNs to be competitive with commonly used structure
learning methods.

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The categories of this paper are: cond-mat
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The categories of this paper are: funct-an
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The categories of this paper are: acc-phys
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The categories of this paper are: math-ph
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The categories of this paper are: physics
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: chem-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: ao-sci
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The categories of this paper are: nucl-ex
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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: solv-int
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The categories of this paper are: adap-org
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The categories of this paper are: astro-ph