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    Deep learning· 2016

    Layer Normalization

    Jimmy Lei Ba (first author) · 2016

    Read the original

    arXiv 1607.06450OpenAlex W3037932933

    On the reading lists

    Why it matters

    Introduced sample-independent normalization, essential for training modern recurrent networks and Transformers.

    Condensed from the expert lists' notes.

    The record

    First released
    2016 (from arXiv 1607.06450)
    Citations
    13,326 citations, as counted by Papers With Code (CC BY-SA 4.0)
    Named by
    both expert lists
    Score
    0.587 · computed rank 164 · consensus 0.66, must-read 0.5, citations percentile 0.826 (2010s · deep-learning), awards 0, existing lists 0.5. 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). Each count names its source. See the method.