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Dropout can be interpreted as variational inference in a Bayesian neural network, where applying random masks approximates sampling from a posterior over weights.
A Variational Autoencoder (VAE) is a probabilistic autoencoder that learns to generate data by inferring hidden causes (latent variables) and decoding them back to observations.