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#### 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 reparameterization 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/VariationalAutoencoder #### Presenter: Yingbo Zhou
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