Non-monotonic sampling schedules never improve upon monotonic baselines in diffusion models, with performance gaps ranging from substantial to negligible depending on the denoiser.
Neural Computation , volume =
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
method 1
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3representative citing papers
CTEM unifies density estimation via a bounded energy-difference transform that yields a sample-only objective with constant target 1, recovering log p without partition functions or unbounded ratio regression.
Reparameterizations create invariances in diffusion inverse-problem solvers, enabling hyperparameter reuse and accelerated inference via the OptDiff optimization framework.
citing papers explorer
-
Is Monotonic Sampling Necessary in Diffusion Models?
Non-monotonic sampling schedules never improve upon monotonic baselines in diffusion models, with performance gaps ranging from substantial to negligible depending on the denoiser.
-
Constant-Target Energy Matching: A Unified Framework for Continuous and Discrete Density Estimation
CTEM unifies density estimation via a bounded energy-difference transform that yields a sample-only objective with constant target 1, recovering log p without partition functions or unbounded ratio regression.
-
Principled Design of Diffusion-based Optimizers for Inverse Problems
Reparameterizations create invariances in diffusion inverse-problem solvers, enabling hyperparameter reuse and accelerated inference via the OptDiff optimization framework.