arXiv cs.CL
· Papers
Spherical Flows for Sampling Categorical Data
arXiv:2605.05629v4 Announce Type: replace-cross Abstract: We study the problem of learning generative models for discrete sequences in a continuous embedding space. Whereas prior approaches typically operate in Euclidean space or on the probability simplex, we instead work on the sphere $mathbb S^{d-1}$. There the von