A projection-based symmetric-manifold diffusion model with spatially-varying covariance reaches near-Euclidean per-step training cost and polynomial sampling guarantees, with faster training and better sample quality on the torus, SO(n), and U(n).
Matching normalizing flows and probability paths on manifolds
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
REJECT 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Efficient Diffusion Models for Symmetric Manifolds
A projection-based symmetric-manifold diffusion model with spatially-varying covariance reaches near-Euclidean per-step training cost and polynomial sampling guarantees, with faster training and better sample quality on the torus, SO(n), and U(n).