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Variants of RMSProp and Adagrad with Logarithmic Regret Bounds.
ICML, (2017): 2545-2553
Adaptive gradient methods have become recently very popular, in particular as they have been shown to be useful in the training of deep neural networks. In this paper we have analyzed RMSProp, originally proposed for the training of deep neural networks, in the context of online convex optimization and show √T-type regret bounds. Moreover...More
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