Phase retrieval: From computational imaging to machine learning: A tutorial
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 …
measurements. As it pervades a broad variety of applications, many researchers have …
Visualizing the loss landscape of neural nets
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 …
loss functions. It is well known that certain network architecture designs (eg, skip …
Spectral methods for data science: A statistical perspective
Spectral methods have emerged as a simple yet surprisingly effective approach for
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …
A geometric analysis of phase retrieval
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 …
of m measurements, y_k=\left| a _k^* x\right| yk= ak∗ x for k= 1, ..., mk= 1,…, m, is it possible …
Adaptive optics for orbital angular momentum-based internet of underwater things applications
Orbital angular momentum (OAM) has the potential to dramatically enhance the amount of
information in the Internet of Underwater Things (IoUT) system. Nevertheless, underwater …
information in the Internet of Underwater Things (IoUT) system. Nevertheless, underwater …
Implicit regularization in nonconvex statistical estimation: Gradient descent converges linearly for phase retrieval and matrix completion
Recent years have seen a flurry of activities in designing provably efficient nonconvex
optimization procedures for solving statistical estimation problems. For various problems like …
optimization procedures for solving statistical estimation problems. For various problems like …
Optimal errors and phase transitions in high-dimensional generalized linear models
Generalized linear models (GLMs) are used in high-dimensional machine learning,
statistics, communications, and signal processing. In this paper we analyze GLMs when the …
statistics, communications, and signal processing. In this paper we analyze GLMs when the …
Wirtinger holography for near-eye displays
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 …
approach for virtual and augmented reality at high-resolution without complex optical setups …
Phase retrieval under a generative prior
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 …
problem}, which asks to recover a signal $ y_0\in\R^ n $ from $ m $ quadratic observations …
Learned hardware-in-the-loop phase retrieval for holographic near-eye displays
P Chakravarthula, E Tseng, T Srivastava… - ACM Transactions on …, 2020 - dl.acm.org
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 …
eyeglasses-style near-eye displays for augmented and virtual reality. However, the image …