The Laplace-Fisher Gate Identity supplies the variance-optimal matrix blending coefficients for Tweedie and target-score estimators under an OU diffusion, enabling improved finite-reference score estimation and posterior density surrogates.
ArXiv abs/2402.06320
3 Pith papers cite this work, alongside 2 external citations. Polarity classification is still indexing.
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IMPFM is a multi-particle flow-map sampling method with sequential posterior sharing and interaction-aware correction that targets a KL-tilted distribution for global exploration in online feedback search.
FES-FM learns a reduced flow-matching transport in collective-variable space to sample free energy surfaces, cutting per-sample generation cost while leaving full-space training cost unchanged.
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Laplace-Fisher Gate Identities for Optimal Matrix-Gated Blended Score Estimation
The Laplace-Fisher Gate Identity supplies the variance-optimal matrix blending coefficients for Tweedie and target-score estimators under an OU diffusion, enabling improved finite-reference score estimation and posterior density surrogates.
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Sequentially-Controlled Interactive Multi-Particle Flow-Maps for Online Feedback-Driven Search
IMPFM is a multi-particle flow-map sampling method with sequential posterior sharing and interaction-aware correction that targets a KL-tilted distribution for global exploration in online feedback search.
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FES-FM: Free Energy Surface Sampling via Reduced Flow Matching
FES-FM learns a reduced flow-matching transport in collective-variable space to sample free energy surfaces, cutting per-sample generation cost while leaving full-space training cost unchanged.