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Unsupervised Learning with Latent Variable Models

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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.

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