26 citations · 56 across the 4 of their papers we have counts for
4 papers
Random Vector Functional Link Networks for Function Approximation on Manifolds
Deanna Needell, Aaron A. Nelson, Rayan Saab +2
The learning speed of feed-forward neural networks is notoriously slow and has presented a bottleneck in deep learning applications for several decades. For instance, gradient-base…
New Algorithms and Improved Guarantees for One-Bit Compressed Sensing on Manifolds
Mark Iwen, Eric Lybrand, Aaron Nelson +1
We study the problem of approximately recovering signals on a manifold from one-bit linear measurements drawn from either a Gaussian ensemble, partial circulant ensemble, or bounde…
Phase retrieval from very few measurements
Matthew Fickus, Dustin G. Mixon, Aaron A. Nelson +1
In many applications, signals are measured according to a linear process, but the phases of these measurements are often unreliable or not available. To reconstruct the signal, one…
Saving phase: Injectivity and stability for phase retrieval
Afonso S. Bandeira, Jameson Cahill, Dustin G. Mixon +1
Recent advances in convex optimization have led to new strides in the phase retrieval problem over finite-dimensional vector spaces. However, certain fundamental questions remain:…