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New Method Preserves Closed-Form Inference in Complex Factor Graphs

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A new research paper introduces a method for composing non-conjugate factor graphs while maintaining closed-form inference. The authors identify five key factor-graph primitives that, when combined, allow for tractable variational message passing. This framework enables universal function approximation with closed-form inference and has been applied to ensemble time-series forecasting, resulting in a Bayesian mixture of experts with inferred gating functions. AI

RANK_REASON The cluster contains a research paper detailing a novel technical approach in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

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New Method Preserves Closed-Form Inference in Complex Factor Graphs

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The cluster contains a research paper detailing a novel technical approach in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Mykola Lukashchuk, Kyrylo Yemets, Wouter M. Kouw, Dmitry Bagaev, \.Ismail \c{S}en\"oz, Jeff Beck, Bert de Vries ·

    Composing Non-Conjugate Factor Graphs with Closed-Form Variational Inference

    arXiv:2605.29467v1 Announce Type: cross Abstract: Stacking probabilistic building blocks into deeper architectures typically breaks closed-form inference. We show that closed-form inference can be preserved. We identify five factor-graph primitives: a bilinear factor, an exponent…

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