323 citations · 1.3k across the 121 of their papers we have counts for
7 papers · 1 filter
Guaranteed sparse signal recovery with highly coherent sensing matrices
Guangliang Chen, Atul Divekar, Deanna Needell
Compressive sensing is a methodology for the reconstruction of sparse or compressible signals using far fewer samples than required by the Nyquist criterion. However, many of the r…
Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm
Deanna Needell, Nathan Srebro, Rachel Ward
We obtain an improved finite-sample guarantee on the linear convergence of stochastic gradient descent for smooth and strongly convex objectives, improving from a quadratic depende…
Kaczmarz Algorithm with Soft Constraints for User Interface Layout
Noreen Jamil, Deanna Needell, Johannes Muller +2
The Kaczmarz method is an iterative method for solving large systems of equations that projects iterates orthogonally onto the solution space of each equation. In contrast to direc…
Greedy Signal Space Methods for incoherence and beyond
Raja Giryes, Deanna Needell
Compressive sampling (CoSa) has provided many methods for signal recovery of signals compressible with respect to an orthonormal basis. However, modern applications have sparked th…
On the Mathematics of Music: From Chords to Fourier Analysis
Nathan Lenssen, Deanna Needell
Mathematics is a far reaching discipline and its tools appear in many applications. In this paper we discuss its role in music and signal processing by revisiting the use of mathem…
Two-Part Reconstruction in Compressed Sensing
Yanting Ma, Dror Baron, Deanna Needell
Two-part reconstruction is a framework for signal recovery in compressed sensing (CS), in which the advantages of two different algorithms are combined. Our framework allows to acc…