Blind inverse problems (i.e. inverse problems with unknown parameters of the forward model)
are well studied for models with uniform grids, such as blind image deconvolution or blind signal
separation. Recently, new methods of learning of non-linear problems with differentiable
nonlinearities (i.e. neural networks) have been proposed, however they rely on supervised
learning on a training set...