Semi-supervised Learning with Deep Generative Models

Semi-supervised Learning withDeep Generative ModelsDiederik P. Kingma , Danilo J. Rezende , Shakir Mohamed , Max Welling Machine Learning Group, Univ. of Amsterdam,{ , Google Deepmind,{danilor, ever-increasing size of modern data sets combined with the difficulty of ob-taining label information has made Semi-supervised Learning one of the problemsof significant practical importance in modern data analysis. We revisit the ap-proach to Semi-supervised Learning with Generative Models and develop new mod-els that allow for effective generalisation from small labelled data sets to largeunlabelled ones. Generative approaches have thus far been either inflexible, in-efficient or non-scalable.}}

approximately invariant to local perturbations along the manifold. The idea of manifold learning ... We show for the first time how variational inference can be brought to bear upon the prob- ... probabilities are formed by a non-linear transformation, with parameters , of a set of latent vari-ables z. This non-linear transformation is ...

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  With, Linear, Model, Time, Learning, Deep, Supervised, Generative, Invariant, Supervised learning with deep generative models

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