Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Asynchronous Parallel Stochastic Gradient Descent - A Numeric Core for
  Scalable Distributed Machine Learning Algorithms   The implementation of a vast majority of machine learning (ML) algorithms
boils down to solving a numerical optimization problem. In this context,
Stochastic Gradient Descent (SGD) methods have long proven to provide good
results, both in terms of convergence and accuracy. Recently, several
parallelization approaches have been proposed in order to scale SGD to solve
very large ML problems. At their core, most of these approaches are following a
map-reduce scheme. This paper presents a novel parallel updating algorithm for
SGD, which utilizes the asynchronous single-sided communication paradigm.
Compared to existing methods, Asynchronous Parallel Stochastic Gradient Descent
(ASGD) provides faster (or at least equal) convergence, close to linear scaling
and stable accuracy.

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The categories of this paper are: math
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The categories of this paper are: plasm-ph
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The categories of this paper are: eess
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The categories of this paper are: q-fin
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The categories of this paper are: gr-qc
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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: astro-ph
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The categories of this paper are: q-bio
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The categories of this paper are: math-ph
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The categories of this paper are: adap-org
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The categories of this paper are: quant-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: physics
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The categories of this paper are: stat
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The categories of this paper are: ao-sci