Auto-Encoding Variational Bayes Auto-Encoding Variational Bayes
Paper summary #### Problem addressed: Variational learning of Bayesian networks #### Summary: This paper present a generic method for learning belief networks, which uses variational lower bound for the likelihood term. #### Novelty: Uses a re-parameterization trick to change random variables to deterministic function plus a noise term, so one can apply normal gradient based learning #### Drawbacks: The resulting model marginal likelihood is still intractible, may not be very good for applications that require the use of actual values of the marginal probablities #### Datasets: MNIST, Frey face #### Additional remarks: Experimentally compared with wake sleep algorithm on logliklihood lower bound as well as estimated marginal likelihood #### Resources: Implementation: https://github.com/y0ast/Variational-Autoencoder #### Presenter: Yingbo Zhou
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Auto-Encoding Variational Bayes
Diederik P Kingma and Max Welling
arXiv e-Print archive - 2013 via Local arXiv
Keywords: stat.ML, cs.LG

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