PnP-CM treats consistency models as plug-and-play priors inside an ADMM solver with noise perturbations and momentum updates to solve linear and nonlinear inverse problems in as few as 2-4 neural function evaluations.
On the convergence of N esterov's accelerated gradient method in stochastic settings
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
SHANG++ delivers faster convergence and stronger robustness to multiplicative noise in stochastic optimization for both convex and strongly convex problems, with explicit parameters and competitive deep-learning results.
citing papers explorer
-
PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems
PnP-CM treats consistency models as plug-and-play priors inside an ADMM solver with noise perturbations and momentum updates to solve linear and nonlinear inverse problems in as few as 2-4 neural function evaluations.
-
SHANG++: Robust Stochastic Acceleration under Multiplicative Noise
SHANG++ delivers faster convergence and stronger robustness to multiplicative noise in stochastic optimization for both convex and strongly convex problems, with explicit parameters and competitive deep-learning results.