Plug-and-Play (PnP) methods are efficient iterative algorithms for solving ill-posed image inverse problems. PnP methods are obtained by using deep Gaussian denois-ers instead of the proximal operator or the gradient-descent step within proximal algorithms.

Bayesian deep learning has always garnered substantial interest in the machine learning community.

Under our framework, the model individualization stems from an optimal internal relation map within the samples themselves.

Phylogenetic trees elucidate evolutionary relationships among species, but phylogenetic inference remains challenging due to the complexity of combining continuous (branch lengths) and discrete parameters (tree topology).