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Research questionCan reducing the complexity of a linear generative prior improve expected reconstruction error in noiseless Gaussian compressed sensing?Compressed sensing can use a family of linear generative priors with different effective complexities, but restricting that prior may affect reconstruction accuracy in ways that differ from ordinary denoising. The key issue is whether lower-complexity priors reduce expected error in the noiseless setting.
AI
Machine Learning
Research Paper
Statistical Machine Learning
Latest papersRecent research connected to this question, newest first.Full-Model Optimality for Tunable Linear Generative Priors in Compressed SensingThe result concerns a tunable family of linear generative priors related through their singular value decompositions, evaluated theoretically in noiseless Gaussian compressed sensing. It compares expected reconstruction error across prior complexities and establishes a result for the full-dimensional member; it does not directly address nonlinear neural priors or noisy measurements.research paper · Sep 2, 2026
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