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      "statement": "# ADADELTA: gradient and update RMS rescaling\n\nReplaced AdaGrad's unbounded squared-gradient accumulation with a decaying average and used prior-update RMS over gradient RMS to form dimensionally matched coordinate updates.\n\n## Scientific scope\n\nInherits AdaGrad and earlier adaptive-step methods. Original removes a manually set global learning rate but retains decay and numerical-stability hyperparameters; no universal tuning-free or convergence guarantee. The Hessian analogy motivates scaling, not actual exact second-order information or guaranteed optimum.\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\nMatthew D. Zeiler\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- [ADADELTA: An Adaptive Learning Rate Method](https://arxiv.org/abs/1212.5701v1)\n",
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