This paper introduces a model-based CS theory that parallels the conventional theory and provides concrete guidelines on how to create model- based recovery algorithms with provable performance guarantees.Expand

We advocate the idea of replacing hand-crafted priors, such as sparsity, with a Generative Adversarial Network (GAN) to solve linear inverse problems such as compressive sensing.Expand

We propose consensus-based distributed SGD (CDSGD) and its momentum variant, CDMSGD) algorithm for collaborative deep learning over fixed topology networks that enables data parallelization as well as decentralized computation.Expand

We introduce a new framework for model-based CS that leverages additional structure in the signal and provides new recovery schemes that can reduce the number of measurements even further.Expand

We consider the problem of recovering a signal x in R^n, from magnitude-only measurements, y_i = |a_i^T x| for i={1,2...m}. Also known as phase retrieval problem, it is a fundamental challenge in nano-, bio- and astronomical imaging systems, astronomical imaging, and speech processing.Expand

We show that with a small number M of random projections of sample points in â„ťN belonging to an unknown K-dimensional Euclidean manifold, the intrinsic dimension (ID) of the sample set can be estimated to high accuracy.Expand