AI RESEARCH
Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling
arXiv CS.LG
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ArXi:2408.06710v3 Announce Type: replace Gaussian Process Latent Variable Models (GPLVMs) have become increasingly popular for unsupervised tasks such as dimensionality reduction and missing data recovery due to their flexibility and non-linear nature. An importance-weighted version of the Bayesian GPLVMs has been proposed to obtain a tighter variational bound.