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Search results with tag "Leibler divergence"
Variational Inference - Princeton University
www.cs.princeton.edu4 Kullback-Leibler Divergence We measure the closeness of the two distributions with Kullback-Leibler (KL) divergence. This comes from information theory, a eld that has deep links to statistics and machine learning. (See the books \Information Theory and Statistics" by Kullback and \Information Theory, Inference, and Learning Algorithms" by ...
T.N.Kipf@uva.nl M.Welling@uva.nl arXiv:1611.07308v1 [stat ...
arxiv.orgwhere KL[q()jjp()] is the Kullback-Leibler divergence between q() and p(). We further take a Gaussian prior p(Z) = Q i p(z i) = Q i N(z i j0;I). For very sparse A, it can be beneļ¬cial to re-weight terms with A ij = 1 in Lor alternatively sub-sample terms with A ij = 0. We choose the former for the following experiments.