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    Deep learning· 2016

    TensorFlow: A system for large-scale machine learning

    Martín Abadi (first author) · 2016

    Read the original

    arXiv 1605.08695OpenAlex W2402144811

    On the reading lists

    Why it matters

    Established computational graph execution across heterogeneous hardware and distributed training infrastructure.

    Condensed from the expert lists' notes.

    The record

    First released
    2016 (from arXiv 1605.08695)
    Citations
    19,933 citations, as counted by Papers With Code (CC BY-SA 4.0)
    Named by
    both expert lists
    Score
    0.714 · computed rank 56 · consensus 0.85, must-read 1, citations percentile 0.884 (2010s · deep-learning), awards 0, existing lists 0.5. Method
    Versions merged
    TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems (editor)

    Editor's notes

    • Merged “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems” into this entry: The 2015 whitepaper and the OSDI 2016 paper describe the same system; one entry, dated from the whitepaper. (AI draft (Claude), not yet reviewed, 2026-10-29)
    • Readability check: fail: A software system description; it teaches the system, not an idea about learning. (AI draft (Claude), not yet reviewed, 2026-10-29)

    Data sources

    Catalogue data from OpenAlex (CC0), Crossref (CC0 metadata; no abstracts are shown), arXiv, and Papers With Code (CC BY-SA 4.0). Each count names its source. See the method.