Search Results for author: Brian M. Kurkoski

Found 6 papers, 2 papers with code

Algebra of L-banded Matrices

no code implementations20 Dec 2022 Shunqi Huang, Lei Liu, Brian M. Kurkoski

Damping is commonly employed to ensure the convergence of iterative algorithms.

Sufficient Statistic Memory Approximate Message Passing

no code implementations23 Jun 2022 Lei Liu, Shunqi Huang, Brian M. Kurkoski

Approximate message passing (AMP) type algorithms have been widely used in the signal reconstruction of certain large random linear systems.

Vocal Bursts Type Prediction

Sufficient-Statistic Memory AMP

no code implementations31 Dec 2021 Lei Liu, Shunqi Huang, Yuzhi Yang, Zhaoyang Zhang, Brian M. Kurkoski

Given an arbitrary MAMP, we can construct an SS-MAMP by damping, which not only ensures the convergence of the state evolution, but also preserves the orthogonality, i. e., its dynamics can be correctly described by state evolution.

Memory Approximate Message Passing

no code implementations4 Jun 2021 Lei Liu, Shunqi Huang, Brian M. Kurkoski

Approximate message passing (AMP) is a low-cost iterative parameter-estimation technique for certain high-dimensional linear systems with non-Gaussian distributions.

Memory AMP

2 code implementations20 Dec 2020 Lei Liu, Shunqi Huang, Brian M. Kurkoski

To asymptotically characterize the performance of BO-MAMP, a state evolution is derived.

Denoising

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