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We show that using a Gaussian process prior in the latent space of a variational autoencoder can improve time series imputation performance, while still allowing for computationally efficient inference through a variational Gauss-Markov process.

We propose a novel version of the classical self-organizing map, which can be used as a structural prior in the latent space of a variational autoencoder, enabling interpretable representations of time series.

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