Small-time score-mixed diffusion dynamics are governed by the geometric potential Φ_λ = λ d1² + (1-λ) d2², reducing the problem to Clarke subgradient inclusions with convergence guarantees in the Dirac-mixture case.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
Lagrangian flow matching reformulates flow matching paths via the least-action principle, recovering optimal-transport and trigonometric diffusion paths as special cases of kinetic and harmonic Lagrangians while enabling new paths.
Pose-LDM generates occluded in-bed images from keypoints to augment training data, achieving top accuracy under severe occlusion compared to other augmentation methods.
citing papers explorer
-
Geometric Asymptotics of Score Mixing and Guidance in Diffusion Models
Small-time score-mixed diffusion dynamics are governed by the geometric potential Φ_λ = λ d1² + (1-λ) d2², reducing the problem to Clarke subgradient inclusions with convergence guarantees in the Dirac-mixture case.
-
Lagrangian Flow Matching: A Least-Action Framework for Principled Path Design
Lagrangian flow matching reformulates flow matching paths via the least-action principle, recovering optimal-transport and trigonometric diffusion paths as special cases of kinetic and harmonic Lagrangians while enabling new paths.
-
Geometry-Conditioned Diffusion for Occlusion-Robust In-Bed Pose Estimation
Pose-LDM generates occluded in-bed images from keypoints to augment training data, achieving top accuracy under severe occlusion compared to other augmentation methods.