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Variational autoencoders trained with q-deformed lower bounds
In training, they exploit the power of variational inference, by optimizing a lower bound on the model evidence.
Learning in Variational Autoencoders with Kullback-Leibler and Renyi Integral Bounds
In this paper we propose two novel bounds for the log-likelihood based on Kullback-Leibler and the R\'{e}nyi divergences, which can be used for variational inference and in particular for the training of Variational AutoEncoders.
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