In recent works, both sparsity-based methods as well as learning-based methods have
proven to be successful in solving several challenging linear inverse problems. However …

Compressive sensing (CS) is a new approach to simultaneous sensing and compression of
sparse and compressible signals based on randomized dimensionality reduction. To …

We propose a novel framework for the deterministic construction of linear, near-isometric
embeddings of a finite set of data points. Given a set of training points X⊂\BBR N, we …

Compressive Sensing (CS) exploits the surprising fact that the information contained in a
sparse signal can be preserved in a small number of compressive, often random linear …

We study–both in theory and practice–the use of momentum motions in classic iterative hard
thresholding (IHT) methods. By simply modifying plain IHT, we investigate its convergence …

Several learning applications require solving high-dimensional regression problems where
the relevant features belong to a small number of (overlap**) groups. For very large …

Suppose that we observe noisy linear measurements of an unknown signal that can be
modeled as the sum of two component signals, each of which arises from a nonlinear …

The nascent field of compressed sensing is founded on the fact that high-dimensional
signals with simple structure can be recovered accurately from just a small number of …

We study the convergence of the iterative projected gradient (IPG) algorithm for arbitrary
(possibly non-convex) sets when both the gradient and projection oracles are computed …

Current popular methods for magnetic resonance fingerprint (MRF) recovery are
bottlenecked by the heavy computations of a matched-filtering step due to the growing size …