University of Cambridge > Talks.cam > Machine Learning @ CUED > Unsupervised Learning with Latent Variable Models

  Browse A-Z

Log in

Cambridge users (raven)details Other usersdetails No account?details

Information on

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

Unsupervised Learning with Latent Variable Models

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact dsu21.

Unsupervised learning is under rapid development. The probabilistic approach is typically based on latent variable models. In this presentation, I will show a number of latent variable models that I have worked on, spaning from parametric to non-parametric and from linear to non-linear. I will show the connection between these models and link to my most recent work: infinite dimensional Gaussian process latent variable models and variational hierarchical communities of experts. I will derive variational lower bounds of these models for efficient inference and show some applications of the developed models on real data.

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

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Computer Laboratory Opera Group Seminars Empowered Voices Year Cambridge Seminars in the History of Cartography ‘Diglossia, Bidialectalism, or Bilingualism? Portuguese as a Foreign Language in the Classroom’ Newnham College MCR Speaker Series The Paykel Lectures

Other talks

Insight into the molecular mechanism of extracellular matrix calcification in the vasculature from NMR spectroscopy and electron microscopy Uncertainty Quantification of geochemical and mechanical compaction in layered sedimentary basins Roland the Hero Developmental cognitive neuroscience
 

© 2006-2024 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity