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Paper detail

A lower bound for the ELBO of the Bernoulli Variational Autoencoder

We consider a variational autoencoder (VAE) for binary data. Our main innovations are an interpretable lower bound for its training objective, a modified initialization and architecture of such a VAE that leads to faster training, and a decision support for finding the appropriate dimension of the latent space via using a PCA. Numerical examples illustrate our theoretical result and the performance of the new architecture.

preprint2020arXivOpen access
Robert SicksRalf KornStefanie Schwaar
Machine Learningmath.STStatistics Theory
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Robert SicksRalf KornStefanie Schwaar

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