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Vector approximate message passing (VAMP) is a computationally simple approach to the recovery of a signal $\mathbf{x}$ from noisy linear measurements $\mathbf{y}=\mathbf{Ax}+\mathbf{w}$.
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J. P. Vila and P. Schniter, “An empirical-Bayes approach to recovering linearly constrained non-negative sparse signals,” IEEE Trans. Signal Processing , vol. 62, no. 18, pp. 4689–4703, Sep. 2014
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J. Ziniel, P. Schniter, and P. Sederberg, “Binary linear classification and feature selection via generalized approximate message passing,” IEEE Trans. Signal Process. , vol. 63, no. 8, pp. 2020–2032, 2015
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2016
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A. K. Fletcher, M. Sahraee-Ardakan, S. Rangan, and P. Schniter, “Expectation consistent approximate inference: Generalizations and convergence,” in Proc. IEEE ISIT , 2016, pp. 190–194
2016
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2016
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G. Reeves and H. D. Pfister, “The replica-symmetric prediction for compressed sensing with Gaussian matrices is exact,” in Proc. IEEE ISIT , 2016
2016
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J. Vila, P. Schniter, S. Rangan, F. Krzakala, and L. Zdeborová, “Adaptive damping and mean removal for the generalized approximate message passing algorithm,” in Proc. IEEE ICASSP , 2015, pp. 2021–2025
2025
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