Tempered Calculus for ML: Application to Hyperbolic Model Embedding
Tempered Calculus for ML: Application to Hyperbolic Model Embedding
Most mathematical distortions used in ML are fundamentally integral in nature: $f$-divergences, Bregman divergences, (regularized) optimal transport distances, integral probability metrics, geodesic distances, etc. In this paper, we unveil a grounded theory and tools which can help improve these distortions to better cope with ML requirements. We start with a …