  The problem of learning a manifold structure on a dataset is framed in terms
of a generative model, to which we use ideas behind autoencoders (namely
adversarial/Wasserstein autoencoders) to fit deep neural networks. From a
machine learning perspective, the resulting structure, an atlas of a manifold,
may be viewed as a combination of dimensionality reduction and "fuzzy"
clustering.
