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This work develops tunable latent priors for diffusion models, normalizing flows, and variational autoencoders, leveraging nested dropout and shows empirically that tunable priors consistently achieve lower reconstruction errors than fixed-complexity baselines.
It is proved that in noiseless Gaussian compressed sensing, the full-dimensional linear prior attains the minimum expected reconstruction error over the entire family of linear priors, indicating that the experimental benefits of tunability in compressed sensing with neural network priors arises due to nonlinearities i...
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