Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Relevance Proximity Graphs for Fast Relevance Retrieval   In plenty of machine learning applications, the most relevant items for a
particular query should be efficiently extracted, while the relevance function
is based on a highly-nonlinear model, e.g., DNNs or GBDTs. Due to the high
computational complexity of such models, exhaustive search is infeasible even
for medium-scale problems. To address this issue, we introduce Relevance
Proximity Graphs (RPG): an efficient non-exhaustive approach that provides a
high-quality approximate solution for maximal relevance retrieval. Namely, we
extend the recent similarity graphs framework to the setting, when there is no
similarity measure defined on item pairs, which is a common practical use-case.
By design, our approach directly maximizes off-the-shelf relevance functions
and does not require any proxy auxiliary models. Via extensive experiments, we
show that the developed method provides excellent retrieval accuracy while
requiring only a few model computations, outperforming indirect models. We
open-source our implementation as well as two large-scale datasets to support
further research on relevance retrieval.

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The categories of this paper are: nlin
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The categories of this paper are: hep-th
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-lat
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The categories of this paper are: funct-an
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The categories of this paper are: stat
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The categories of this paper are: nucl-ex
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The categories of this paper are: gr-qc
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The categories of this paper are: eess
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The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-th
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The categories of this paper are: math-ph
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The categories of this paper are: econ
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The categories of this paper are: acc-phys
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The categories of this paper are: alg-geom
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The categories of this paper are: comp-gas