Graph embeddings in Hyperbolic Space

I gave a talk last night at the Berlin machine learning meetup on learning graph embeddings in hyperbolic space, featuring the recent NIPS 2017 paper of Nickel & Kiela. Covered are:

• An illustration of why the Euclidean plane is not a good place to embed trees (since circle circumference grows only linearly in the radius);
• Extending this same argument to higher dimensional Euclidean space;
• An introduction to the hyperbolic plane and the Poincaré disc model;
• A discussion of Rik Sarkar’s result that trees embed with arbitrarily small error in the hyperbolic plane;
• A demonstration that, in the hyperbolic plane, circle circumference is exponential in the radius (better written here);
• A review of the results of Nickel & Kiela on the (transitive closure of the) WordNet hypernymy graph;
• Some thoughts on the gradient optimisation (perhaps better written here).

And here are the slides!