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      "statement": "# DistBelief: model/data parallelism with Downpour and Sandblaster\n\nPartitioned neural computation across machines and replicas, combining asynchronous Downpour SGD and coordinated distributed batch optimization through sharded parameter storage.\n\n## Scientific scope\n\nSystem/algorithm integration inherits SGD, L-BFGS, Adagrad and earlier distributed optimization, not their invention or first parameter-server concept. Scaling benefits depend on connectivity, resource allocation, communication and staleness; not unconstrained neural convergence or universal speedup.\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\nJeffrey Dean; Greg S. Corrado; Rajat Monga; Kai Chen; Matthieu Devin; Quoc V. Le; Mark Z. Mao; Marc’Aurelio Ranzato; Andrew Senior; Paul Tucker; Ke Yang; Andrew Y. Ng\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- [Large Scale Distributed Deep Networks](https://proceedings.neurips.cc/paper/2012/file/6aca97005c68f1206823815f66102863-Paper.pdf)\n",
      "summary": "Partitioned neural computation across machines and replicas, combining asynchronous Downpour SGD and coordinated distributed batch optimization through sharded parameter storage.",
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