What is recorded here?
Replaced AdaGrad's unbounded squared-gradient accumulation with a decaying average and used prior-update RMS over gradient RMS to form dimensionally matched coordinate updates.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Inherits 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.
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People and systems
Matthew D. Zeiler
Source-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.
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English catalogue narrative
ADADELTA: gradient and update RMS rescaling
Replaced AdaGrad's unbounded squared-gradient accumulation with a decaying average and used prior-update RMS over gradient RMS to form dimensionally matched coordinate updates.
Scientific scope
Inherits 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.
Reading status
This 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.
People and systems
Matthew D. Zeiler
Source-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.