Alternating optimization for MI-optimal density control of linear systems coincides with that for generalized Schrödinger bridges.
Gen- eralized schr \” odinger bridge matching
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A forward-backward HJB duality computes the optimal stochastic transport control from easy forward relaxation trajectories alone, expressed as path-space free energy without backward simulation.
DPDL learns multiple Gaussian prototypes and a Schrödinger bridge diffusion process to enclose normal samples in a compact discriminative space while using hyperspherical dispersion to identify out-of-distribution anomalies, reporting SOTA results on 9 datasets.
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Mutual Information Optimal Density Control of Linear Systems and Generalized Schr\"{o}dinger Bridges with Reference Refinement
Alternating optimization for MI-optimal density control of linear systems coincides with that for generalized Schrödinger bridges.
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Generative optimal transport via forward-backward HJB matching
A forward-backward HJB duality computes the optimal stochastic transport control from easy forward relaxation trajectories alone, expressed as path-space free energy without backward simulation.
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Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection
DPDL learns multiple Gaussian prototypes and a Schrödinger bridge diffusion process to enclose normal samples in a compact discriminative space while using hyperspherical dispersion to identify out-of-distribution anomalies, reporting SOTA results on 9 datasets.