A New Message Passing Algorithm Based on Sphere Decoding Improvement

2021 ◽  
pp. 685-695
Author(s):  
Hongwei Zhao ◽  
Yue Yan ◽  
Zichun Zhang
Keyword(s):  
Message Passing ◽  
Sphere Decoding ◽  
Message Passing Algorithm

scholarly journals Cooperative Localization for Mobile Networks: A Distributed Belief Propagation–Mean Field Message Passing Algorithm

2016 ◽  
Vol 23 (6) ◽  
pp. 828-832 ◽  
Author(s):  
Burak Cakmak ◽  
Daniel N. Urup ◽  
Florian Meyer ◽  
Troels Pedersen ◽  
Bernard H. Fleury ◽  
...  
Keyword(s):  
Mobile Networks ◽  
Message Passing ◽  
Belief Propagation ◽  
Mean Field ◽  
Cooperative Localization ◽  
Message Passing Algorithm

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.

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