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{{Main|Variational autoencoder}}
[[Variational autoencoder]]s (VAEs) belong to the families of [[variational Bayesian methods]]. Despite the architectural similarities with basic autoencoders, VAEs are
Given an input dataset <math>x</math> characterized by an unknown probability function <math>P(x)</math> and a multivariate latent encoding vector <math>z</math>, the objective is to model the data as a distribution <math>p_\theta(x)</math>, with <math>\theta</math> defined as the set of the network parameters so that <math>p_\theta(x) = \int_{z}p_\theta(x,z)dz </math>.
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