We prove clean optimality results for clustering in ultrametrics, identify ways to take advantage of this theory and thoroughly evaluate the resulting techniques.

We introduce Deep Taxonomic Networks, a deep latent variable approach that uses a complete binary-tree mixture-of-Gaussians prior in a VAE framework to discover interpretable hierarchical taxonomies and prototype clusters from unlabeled data