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      "statement": "# Pruning and Deep Compression: retraining, weight sharing and entropy coding\n\nPruned small-magnitude trained connections and retrained survivors, then combined pruning with quantized weight sharing/codebook retraining and Huffman coding to reduce stored model size.\n\n## Scientific scope\n\nBundles the2015 pruning precursor and compression pipeline with distinct four/three-author bylines. Inherits earlier pruning/quantization/entropy-coding techniques; no invention of all pruning. Storage savings and retained accuracy are model/evaluation-specific, not guaranteed latency or energy reduction on dense hardware. First preprint title differs from laterDeep Compression title.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nSong Han; Jeff Pool; John Tran; William J. Dally; Huizi Mao\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Learning both Weights and Connections for Efficient Neural Networks](https://arxiv.org/abs/1506.02626v1)\n- [A Deep Neural Network Compression Pipeline: Pruning, Quantization, Huffman Encoding](https://arxiv.org/abs/1510.00149v1)\n",
      "summary": "Pruned small-magnitude trained connections and retrained survivors, then combined pruning with quantized weight sharing/codebook retraining and Huffman coding to reduce stored model size.",
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