Exploiting Restricted Boltzmann Machines and Deep Belief Networks in Compressed Sensing

This paper proposes a CS scheme that exploits the representational power of restricted Boltzmann machines and deep learning architectures to model the prior distribution of the sparsity pattern of signals belonging to the same class. The determined probability distribution is then used in a maximum a posteriori (MAP) approach for the reconstruction... (read more)

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