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Variant using a discrete dictionary of latent vectors, combining advantages of autoencoders and categorical representations.

VAE augmented with normalizing flows to model more complex latent distributions than a simple Gaussian.

Application of the information bottleneck principle in a variational framework for learning optimal compressed representations.

Improvement of VAE using importance sampling to obtain a tighter bound on log-likelihood.

Learned prior distribution parametrically as a mixture of encoder outputs on pseudo-inputs.

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