Learning and free energies for vector approximate message passing

@article{Fletcher2017LearningAF,
  title={Learning and free energies for vector approximate message passing},
  author={Alyson K. Fletcher and Philip Schniter},
  journal={2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2017},
  pages={4247-4251}
}
  • Alyson K. Fletcher, Philip Schniter
  • Published 2017
  • Mathematics, Computer Science
  • 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • Vector approximate message passing (VAMP) is a computationally simple approach to the recovery of a signal x from noisy linear measurements y = Ax + w. Like the AMP proposed by Donoho, Maleki, and Montanari in 2009, VAMP is characterized by a rigorous state evolution (SE) that holds under certain large random matrices and that matches the replica prediction of optimality. But while AMP's SE holds only for large i.i.d. sub-Gaussian A, VAMP's SE holds under the much larger class: right… CONTINUE READING
    Fuel cell current ripple minimization using a bi-buck power interface
    10
    Approximate Message Passing for Downlink Sparse Channel Estimation in FDD Massive MIMO
    Rigorous Dynamics and Consistent Estimation in Arbitrarily Conditioned Linear Systems
    6

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 70 REFERENCES
    Improvements to Platt's SMO Algorithm for SVM Classifier Design
    1637
    Distribution de la florine alpine dans la bassin de dranses et dans quelques regiones voisines
    • 1901
    Delayed Stabilization of Dendritic Spines in Fragile X Mice
    248
    Regenerative simulation in heavy traffic
    • 1990
    Belowground carbon allocation in unfertilized and fertilized red pine plantations in northern Wisconsin.
    400
    DNA "Fossils" and Phylogenetic Analysis
    43