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stat.ML2026
Full-Model Optimality for Tunable Linear Generative Priors in Compressed Sensing
Zhaoming Li, Paul Hand
Generative models have been studied experimentally and theoretically as priors for inverse problems such as compressed sensing. Recent work by Gunn et al. studied the use of genera…
cs.LG2026
Tunable Latent Generative Priors for Compressed Sensing and Inverse Problems
Sean Gunn, Jorio Cocola, Oliver De Candido +2
Latent generative models have emerged as powerful priors for solving inverse problems. These models typically represent a class of natural signals at a single, fixed complexity, go…