autoencoders
A type of machine learning [[architecture]] that encodes data into a self-learned representation.
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variational autoencoder
A probabilistic [[architecture]] from the family of [[autoencoders]]. The self-learned representation is used to parameterize a probability distribution (i.e. the [[posterior distribution]]), from which a decoder can draw samples from to generate a range of outputs. We can either directly predict the mean of the distribution, or perform sampling over the distribution to obtain #uncertainty estimates.