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Full-Model Optimality for Tunable Linear Generative Priors in Compressed Sensing

This story is from 2026-09-03. It is preserved in the archive; the latest stories are on the live feed.

arXiv:2609.02790v1 Announce Type: new Abstract: 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 generative priors with tunable complexity, where a family of generative prior…

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  1. 2026-09-03 04:00 · arXiv stat.ML
    Full-Model Optimality for Tunable Linear Generative Priors in Compressed Sensing

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