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A Statistical Framework to Investigate the Optimality of Signal-Reconstruction Methods
We present a statistical framework to benchmark the performance of reconstruction algorithms for linear inverse problems, in particular, neural-network-based methods that require large quantities of training data.
Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling
We propose a generalized Gibbs sampler algorithm for obtaining samples approximately distributed from a high-dimensional Gaussian distribution.
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