Hyperbolic Manifolds in Deep Learning I

Abstract: Hyperbolic manifolds are quite new in deep learning. Mathematical elegance and theoretical advantages are very attractive properties for dimensionality reduction and rich representations. Moreover, a lot of research was done to investigate opportunities in graph-based deep learning or language models. In the talk I’ll give an overview of what are the main advances in the area, highlighting the most problematic theory and motivation. During the practical session, we’ll get familiar with models and implementations that make use of the hyperbolic space to their fullest potential.

machine learningMathematics

Audience: researchers in the discipline

Workshop on Geometry and Machine Learning