  We propose a model-agnostic pipeline to recover graph signals from an expert
system by exploiting the content addressable memory property of restricted
Boltzmann machine and the representational ability of a neural network. The
proposed pipeline requires the deep neural network that is trained on a
downward machine learning task with clean data, data which is free from any
form of corruption or incompletion. We show that denoising the representations
learned by the deep neural networks is usually more effective than denoising
the data itself. Although this pipeline can deal with noise in any dataset, it
is particularly effective for graph-structured datasets.
