Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bayesian Nonparametric Boolean Factor Models   We build upon probabilistic models for Boolean Matrix and Boolean Tensor
factorisation that have recently been shown to solve these problems with
unprecedented accuracy and to enable posterior inference to scale to Billions
of observation. Here, we lift the restriction of a pre-specified number of
latent dimensions by introducing an Indian Buffet Process prior over factor
matrices. Not only does the full factor-conditional take a computationally
convenient form due to the logical dependencies in the model, but also the
posterior over the number of non-zero latent dimensions is remarkably simple.
It amounts to counting the number false and true negative predictions, whereas
positive predictions can be ignored. This constitutes a very transparent
example of sampling-based posterior inference with an IBP prior and,
importantly, lets us maintain extremely efficient inference. We discuss
applications to simulated data, as well as to a real world data matrix with 6
Million entries.

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The categories of this paper are: ao-sci
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The categories of this paper are: quant-ph
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The categories of this paper are: physics
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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: nlin
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The categories of this paper are: chem-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: eess
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The categories of this paper are: funct-an
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The categories of this paper are: hep-lat
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The categories of this paper are: q-bio
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The categories of this paper are: cmp-lg
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-ex
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The categories of this paper are: gr-qc