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    Deep learning· 2014

    Scaling Distributed Machine Learning with the Parameter Server

    Mu Li (first author) · 2014

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    DOI 10.1145/2640087.2644155OpenAlex W2083842231

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    Why it matters

    Established scalable parameter-server architecture with asynchronous communication, flexible consistency, and fault tolerance.

    Condensed from the expert lists' notes.

    The record

    First released
    2014 (from Crossref)
    Citations
    2,024 citations, as counted by Semantic Scholar logoSemantic Scholar (non-commercial licence)
    Named by
    both expert lists
    Score
    0.304 · computed rank 599 · consensus 0.66, must-read 0, citations percentile 0.302 (2010s · deep-learning), awards 0, existing lists 0. Method

    Data sources

    Catalogue data from OpenAlex (CC0), Crossref (CC0 metadata; no abstracts are shown), arXiv, and Papers With Code (CC BY-SA 4.0), and Semantic Scholar logoSemantic Scholar (used under its non-commercial API licence). Each count names its source. See the method.