Gradient Information for Representation and Modeling

NeurIPS 2019 Jie DingRobert CalderbankVahid Tarokh

Motivated by Fisher divergence, in this paper we present a new set of information quantities which we refer to as gradient information. These measures serve as surrogates for classical information measures such as those based on logarithmic loss, Kullback-Leibler divergence, directed Shannon information, etc... (read more)

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