Phase retrieval consists in the recovery of a complex-valued signal from intensity-only
measurements. As it pervades a broad variety of applications, many researchers have …

Neural network training relies on our ability to find" good" minimizers of highly non-convex
loss functions. It is well known that certain network architecture designs (eg, skip …

Spectral methods have emerged as a simple yet surprisingly effective approach for
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …

Can we recover a complex signal from its Fourier magnitudes? More generally, given a set
of m measurements, y_k=\left| a _k^* x\right| yk= ak∗ x for k= 1, ..., mk= 1,…, m, is it possible …

Recent years have seen a flurry of activities in designing provably efficient nonconvex
optimization procedures for solving statistical estimation problems. For various problems like …

Generalized linear models (GLMs) are used in high-dimensional machine learning,
statistics, communications, and signal processing. In this paper we analyze GLMs when the …

Near-eye displays using holographic projection are emerging as an exciting display
approach for virtual and augmented reality at high-resolution without complex optical setups …

Phase retrieval algorithms have become an important component in many modern
computational imaging systems. For instance, in the context of ptychography and speckle …

We introduce a novel deep-learning inspired formulation of the\textit {phase retrieval
problem}, which asks to recover a signal $ y_0\in\R^ n $ from $ m $ quadratic observations …

Holography is arguably the most promising technology to provide wide field-of-view compact
eyeglasses-style near-eye displays for augmented and virtual reality. However, the image …