Phase Retrieval Meets Statistical Learning Theory: A Flexible Convex Relaxation

13 October 2016

Papers citing "Phase Retrieval Meets Statistical Learning Theory: A Flexible Convex Relaxation"

11 / 11 papers shown

Title
Stochastic Light Field Holography Florian Schiffers Praneeth Chakravarthula N. Matsuda Grace Kuo Ethan Tseng Douglas Lanman Felix Heide O. Cossairt 11 5 0 12 Jul 2023
Misspecified Phase Retrieval with Generative Priors Zhaoqiang Liu Xinshao Wang Jiulong Liu 38 4 0 11 Oct 2022
Spectral Methods for Data Science: A Statistical Perspective Yuxin Chen Yuejie Chi Jianqing Fan Cong Ma 29 165 0 15 Dec 2020
Deep S $^3$ PR: Simultaneous Source Separation and Phase Retrieval Using Deep Generative Models Christopher A. Metzler Gordon Wetzstein 20 11 0 14 Feb 2020
Online Stochastic Gradient Descent with Arbitrary Initialization Solves Non-smooth, Non-convex Phase Retrieval Yan Shuo Tan Roman Vershynin 19 35 0 28 Oct 2019
prDeep: Robust Phase Retrieval with a Flexible Deep Network Christopher A. Metzler Philip Schniter Ashok Veeraraghavan Richard G. Baraniuk OOD 31 168 0 01 Mar 2018
A Precise Analysis of PhaseMax in Phase Retrieval Fariborz Salehi Ehsan Abbasi B. Hassibi 14 22 0 20 Jan 2018
Lifting high-dimensional nonlinear models with Gaussian regressors Christos Thrampoulidis A. S. Rawat 10 8 0 11 Dec 2017
Blind Gain and Phase Calibration via Sparse Spectral Methods Yanjun Li Kiryung Lee Y. Bresler 19 27 0 30 Nov 2017
Solving Almost all Systems of Random Quadratic Equations G. Wang G. Giannakis Y. Saad Jie Chen 26 25 0 29 May 2017
Phase Transitions of Spectral Initialization for High-Dimensional Nonconvex Estimation Yue M. Lu Gen Li 15 89 0 21 Feb 2017