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Meta-descent for Online, Continual Prediction

THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF AR..., pp.3943-3950, (2019)

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This paper investigates different vector step-size adaptation approaches for non-stationary online, continual prediction problems. Vanilla stochastic gradient descent can be considerably improved by scaling the update with a vector of appropriately chosen step-sizes. Many methods, including AdaGrad, RMSProp, and AMSGrad, keep statistics a...更多

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Jacobsen Andrew
Jacobsen Andrew
Schlegel Matthew
Schlegel Matthew
Linke Cameron
Linke Cameron
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