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Variational Bayesian Dropout with a Hierarchical Prior
Then, we incorporate such a prior into inferring the joint posterior over network weights and the variance in the hierarchical prior, with which both the network training and the dropout rate estimation can be cast into a joint optimization problem.
Deblurring Natural Image Using Super-Gaussian Fields
Existing sparsity-based priors are usually rooted in modeling the response of images to some specific filters (e. g., image gradients), which are insufficient to capture the complicated image structures.
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