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Approximating the Kullback-Leibler Divergence Between GMMs

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Approximating the Kullback-Leibler divergence between Gaussian mixture models

The Kullback-Leibler divergence is a widely used tool. Computing the KL divergence between two Gaussian mixture models analytically is not computationally tractable. Hershey and Olsen (IEEE International Conference on Acoustics, Speech, and Signal Processing 2007, discuss various ways of approximating it.

This talk is part of the Machine Learning Reading Group @ CUED series.

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