Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series

I got mildly involved in a cool project with the ETHZ group, lead by Vincent Fortuin and Matthias Hüser, along with Francesco Locatello, myself, and Gunnar Rätsch. The work is about building a variational autoencoder with a discrete (and thus interpretable) latent space that admits topological neighbourhood structure through using a self organising map. To represent latent dynamics (the lab is interested in time series modelling), there also is a built-in Markov transition model. We just put a version on arXiv.

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