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    Deep learning· 2015

    Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

    Song Han (first author) · 2015 · published 2016

    Read the original

    arXiv 1510.00149OpenAlex W2119144962

    On the reading lists

    Why it matters

    Unified pruning, quantization, and entropy coding into a landmark neural-network compression pipeline.

    Condensed from the expert lists' notes.

    The record

    First released
    2015 (from arXiv 1510.00149)
    Published
    2016
    Citations
    10,709 citations, as counted by Semantic Scholar logoSemantic Scholar (non-commercial licence)
    Named by
    both expert lists
    Score
    0.627 · computed rank 128 · consensus 0.89, must-read 0.5, citations percentile 0.744 (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), and Semantic Scholar logoSemantic Scholar (used under its non-commercial API licence). Each count names its source. See the method.