Message passing methods are often applied for approximate inference whenever the problem prohibits exact inference. Belief propagation (BP) and variants there of constitute a group of message passing methods that often work remarkably well in many applications. The overarching aim of this project is to better understand the capabilities and limitations of BP in the context of MIMO signal detection and to subsequently enhance BP for this specific application.
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Conference paper Fuchs A. , Knoll C. , Leitinger E. & Pernkopf F. (2023) Self-attention for enhanced OAMP Detection in MIMO Systems . in 48th IEEE International Conference on Acoustics, Speech, and Signal Processing . [more info ] [doi ]Conference paper Knoll C. & Pernkopf F. (2023) Reliable Belief Propagation: Recent Theoretical and Practical Advances . in 33rd IEEE International Workshop on Machine Learning for Signal Processing . [more info ] [doi ]Journal article Knoll C. , Weller A. & Pernkopf F. (2022) Self-Guided Belief Propagation – a Homotopy Continuation Method . in IEEE Transactions on Pattern Analysis and Machine Intelligence , 2022 , p. 1-18. [more info ] [doi ]Conference paper Leisenberger H. , Pernkopf F. & Knoll C. (2022) Fixing the Bethe Approximation: How Structural Modifications in a Graph Improve Belief Propagation . in 38th Conference on Uncertainty in Artificial Intelligence (pp. 1085–1095). [more info ]Conference paper Leisenberger H. , Knoll C. , Seeber R. & Pernkopf F. (2021) Convergence Behavior of Belief Propagation: Estimating Regions of Attraction via Lyapunov Functions . in 37th Conference on Uncertainty in Artificial Intelligence (pp. 1863-1873). [more info ]Journal article Knoll C. & Pernkopf F. (2020) Belief propagation: accurate marginals or accurate partition function—where is the difference? . in Journal of Statistical Mechanics: Theory and Experiment , 2020(12) . [more info ] [doi ]