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Sparse superposition codes, or sparse regression codes, constitute a new class of codes which was first introduced for communication over the additive white Gaussian noise (AWGN) channel.
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C. Rush, A. Greig, and R. Venkataramanan, “Capacity-achieving sparse superposition codes via approximate message passing decoding,” IEEE Transactions on Information Theory , vol. 63, no. 3, pp. 1476–1500, March 2017
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J. Barbier and F. Krzakala, “Approximate message-passing decoder and capacity achieving sparse superposition codes,” IEEE Transactions on Information Theory , vol. 63, no. 8, pp. 4894–4927, 2017
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——, “The error exponent of sparse regression codes with amp decoding,” in 2017 IEEE International Symposium on Information Theory (ISIT) , June 2017, pp. 2478–2482
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M. Dia, “High-dimensional inference on dense graphs with applications to coding theory and machine learning,” Ph.D. dissertation, EPFL IC School, Lausanne, 2018
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J. Barbier, F. Krzakala, N. Macris, L. Miolane, and L. Zdeborová, “Optimal errors and phase transitions in high-dimensional generalized linear models,” in Proceedings of the 31st Conference On Learning Theory , ser. Proceedings of Machine Learning Research, vol. 75. PMLR, July 2018, pp. 728–731
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A. Greig and R. Venkataramanan, “Techniques for improving the finite length performance of sparse superposition codes,” IEEE Transactions on Communications , vol. 66, no. 3, pp. 905–917, March 2018
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