Develops error-propagation bounds and stability estimates for probability-flow ODE distillation, yielding a stability-balanced non-uniform time discretization that improves few-step sampling accuracy.
Expressive power of deep networks on manifolds: Simultaneous approximation
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
CNNs achieve dimension-dependent Sobolev approximation rates on manifolds, and a spectral boundary loss using Laplace-Beltrami eigenmodes enables stable PINN solutions for elliptic problems with improved accuracy over standard approaches.
citing papers explorer
-
A Quantitative Approximation Framework for Flow Distillation in Diffusion Models
Develops error-propagation bounds and stability estimates for probability-flow ODE distillation, yielding a stability-balanced non-uniform time discretization that improves few-step sampling accuracy.