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Abstract
Belief propagation (BP) and the Bethe approximation are two closely relatedconcepts that both suffer from the existence of multiple fixed points (or stationarypoints). We propose a modification of BP, termed self-guided belief propagation(SBP), that incorporates the pairwise potentials only gradually; this essentiallyselects one specific fixed point and increases the accuracy without increasing thecomputational burden. We apply SBP to various models with Ising potentials andshow that: (i) SBP is superior in terms of accuracy whenever BP converges, and (ii)SBP obtains a unique, stable, and accurate solution whenever BP does not converge.
Originalsprache | englisch |
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Seitenumfang | 6 |
Publikationsstatus | Veröffentlicht - 14 Dez. 2019 |
Veranstaltung | Machine Learning and the Physical Sciences - Vancouver, Kanada Dauer: 14 Dez. 2019 → 14 Dez. 2019 |
Konferenz
Konferenz | Machine Learning and the Physical Sciences |
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Land/Gebiet | Kanada |
Ort | Vancouver |
Zeitraum | 14/12/19 → 14/12/19 |
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Guided Selection of Accurate Belief Propagation Fixed Points
Christian Knoll (Redner/in)
14 Dez. 2019Aktivität: Vortrag oder Präsentation › Posterpräsentation › Science to science