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Information Theoretic Limits for Phase Retrieval with Subsampled Haar
  Sensing Matrices
v1v2 (latest)

Information Theoretic Limits for Phase Retrieval with Subsampled Haar Sensing Matrices

IEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2019
25 October 2019
Rishabh Dudeja
Junjie Ma
A. Maleki
ArXiv (abs)PDFHTML

Papers citing "Information Theoretic Limits for Phase Retrieval with Subsampled Haar Sensing Matrices"

4 / 4 papers shown
The price of ignorance: how much does it cost to forget noise structure
  in low-rank matrix estimation?
The price of ignorance: how much does it cost to forget noise structure in low-rank matrix estimation?Neural Information Processing Systems (NeurIPS), 2022
Jean Barbier
Tianqi Hou
Marco Mondelli
Manuel Sáenz
439
21
0
20 May 2022
Estimation in Rotationally Invariant Generalized Linear Models via
  Approximate Message Passing
Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing
R. Venkataramanan
Kevin Kögler
Marco Mondelli
343
35
0
08 Dec 2021
Universality of Linearized Message Passing for Phase Retrieval with
  Structured Sensing Matrices
Universality of Linearized Message Passing for Phase Retrieval with Structured Sensing Matrices
Rishabh Dudeja
Milad Bakhshizadeh
474
13
0
24 Aug 2020
Exact asymptotics for phase retrieval and compressed sensing with random
  generative priors
Exact asymptotics for phase retrieval and compressed sensing with random generative priorsMathematical and Scientific Machine Learning (MSML), 2019
Benjamin Aubin
Bruno Loureiro
Antoine Baker
Florent Krzakala
Lenka Zdeborová
364
39
0
04 Dec 2019
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