Turbo Message Passing Algorithms for Structured Signal Recovery

2020 ◽  
Author(s):  
Xiaojun Yuan ◽  
Zhipeng Xue
Keyword(s):  
Message Passing ◽  
Signal Recovery ◽  
Message Passing Algorithms

Improved Message Passing Algorithms for Sparse Code Multiple Access

2017 ◽  
Vol 66 (11) ◽  
pp. 9986-9999 ◽  
Author(s):  
Jincheng Dai ◽  
Kai Niu ◽  
Chao Dong ◽  
Jiaru Lin
Keyword(s):  
Message Passing ◽  
Multiple Access ◽  
Sparse Code ◽  
Message Passing Algorithms

scholarly journals Message-Passing Algorithms: Reparameterizations and Splittings

2013 ◽  
Vol 59 (9) ◽  
pp. 5860-5881 ◽  
Author(s):  
Nicholas Ruozzi ◽  
Sekhar Tatikonda
Keyword(s):  
Message Passing ◽  
Message Passing Algorithms

Message Passing Algorithms for Scalable Multitarget Tracking

2018 ◽  
Vol 106 (2) ◽  
pp. 221-259 ◽  
Author(s):  
Florian Meyer ◽  
Thomas Kropfreiter ◽  
Jason L. Williams ◽  
Roslyn Lau ◽  
Franz Hlawatsch ◽  
...  
Keyword(s):  
Message Passing ◽  
Multitarget Tracking ◽  
Message Passing Algorithms

Matrix inference and estimation in multi-layer models*

2021 ◽  
Vol 2021 (12) ◽  
pp. 124004
Author(s):  
Parthe Pandit ◽  
Mojtaba Sahraee-Ardakan ◽  
Sundeep Rangan ◽  
Philip Schniter ◽  
Alyson K Fletcher
Keyword(s):  
Neural Network ◽  
Message Passing ◽  
Hidden Variables ◽  
Signal Recovery ◽  
Inference Problem ◽  
Neural Network Learning ◽  
Approximate Message Passing ◽  
Message Passing Algorithm ◽  
Training Samples ◽  
Mixed Regression

Abstract We consider the problem of estimating the input and hidden variables of a stochastic multi-layer neural network (NN) from an observation of the output. The hidden variables in each layer are represented as matrices with statistical interactions along both rows as well as columns. This problem applies to matrix imputation, signal recovery via deep generative prior models, multi-task and mixed regression, and learning certain classes of two-layer NNs. We extend a recently-developed algorithm—multi-layer vector approximate message passing, for this matrix-valued inference problem. It is shown that the performance of the proposed multi-layer matrix vector approximate message passing algorithm can be exactly predicted in a certain random large-system limit, where the dimensions N × d of the unknown quantities grow as N → ∞ with d fixed. In the two-layer neural-network learning problem, this scaling corresponds to the case where the number of input features as well as training samples grow to infinity but the number of hidden nodes stays fixed. The analysis enables a precise prediction of the parameter and test error of the learning.

Mobile Agent Algorithms Versus Message Passing Algorithms

2006 ◽  
pp. 187-201 ◽  
Author(s):  
J. Chalopin ◽  
E. Godard ◽  
Y. Métivier ◽  
R. Ossamy
Keyword(s):  
Mobile Agent ◽  
Message Passing ◽  
Message Passing Algorithms
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