University of Cambridge > Talks.cam > Machine Learning Reading Group @ CUED > Approximating the Kullback-Leibler Divergence Between GMMs

  Browse A-Z

Log in

Googledetails Microsoftdetails

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹī¸ Information

Approximating the Kullback-Leibler Divergence Between GMMs

Download to your calendar using vCal

If you have a question about this talk, please contact Shakir Mohamed .

Approximating the Kullback-Leibler divergence between Gaussian mixture models http://pederao.googlepages.com/KLdiv.pdf

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, http://pederao.googlepages.com/KLdiv.pdf) discuss various ways of approximating it.

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

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Milner Therapeutics Institute Seminar Room, The Fitzwilliam Museum Webinar & FW11, Computer Laboratory, William Gates Building.

Other talks

Talk title TBC Synthesizing Diverse Policies for Multi-Agent Coordination Talk TBC Morning Tea and Coffee Summer Cactus & Succulent Show Polar Oceans Seminar Talk
 

Š 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity