Work done for the Spring 2019 class of Geometric Methods in ML at ENSAE. The instructor was Marco Cuturi.
The report starts with a review of some optimal transport topics (Wasserstein spaces and Sinkhorn divergences). It then moves on to the problem of learning Wasserstein embeddings, with a focus on word embeddings. It finishes with a thorough study of word2cloud. We provide our own implementation, and the results we obtained are very promising.
Final grade: 20/20
Frogner et al, Learning Embeddings into Entropic Wasserstein Spaces [link]