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• W1574502566 abstract "A recurring problem when building probabilistic latent variable models is regularization and model selection, for instance, the choice of the dimensionality of the latent space. In the context of belief networks with latent variables, this problem has been adressed with Automatic Relevance Determination (ARD) employing Monte Carlo inference. We present a variational inference approach to ARD for Deep Generative Models using doubly stochastic variational inference to provide fast and scalable learning. We show empirical results on a standard dataset illustrating the effects of contracting the latent space automatically. We show that the resulting latent representations are significantly more compact without loss of expressive power of the learned models." @default.
• W1574502566 created "2016-06-24" @default.
• W1574502566 creator A5035416263 @default.
• W1574502566 creator A5064498575 @default.
• W1574502566 date "2015-05-28" @default.
• W1574502566 modified "2023-09-27" @default.
• W1574502566 title "Automatic Relevance Determination For Deep Generative Models" @default.
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• W1574502566 hasPublicationYear "2015" @default.
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