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  • title: Model Selection for Bayesian Autoencoders
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            Model Selection for Bayesian Autoencoders
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            Model Selection for Bayesian Autoencoders

            Dec 6, 2021

            Speakers

            BT

            Ba-Hien Tran

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            SR

            Simone Rossi

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            DM

            Dimitrios Milios

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            About

            We develop a novel method for carrying out model selection for Bayesian autoencoders (BAEs) by means of prior hyper-parameter optimization. Inspired by the common practice of type-II maximum likelihood optimization and its equivalence to Kullback-Leibler divergence minimization, we propose to optimize the distributional sliced-Wasserstein distance (DSWD) between the output of the autoencoder and the empirical data distribution. The advantages of this formulation are that we can estimate the DSWD…

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            NeurIPS 2021

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