An analytic uniform ergodic latent trajectory is pushed forward by a conditional flow matching map to produce asymptotically ergodic trajectories matching any target density with provable coverage bounds.
and Cuturi, M.Computational optimal transport: With applications to data science
2 Pith papers cite this work. Polarity classification is still indexing.
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TransSplat uses unbalanced semantic transport to match edited 2D evidence with 3D Gaussians and recover a shared 3D edit field, yielding better local accuracy and structural consistency than prior view-consistency methods.
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Ergodic Trajectory Design by Learned Pushforward Maps: Provable Coverage via Conditional Flow Matching
An analytic uniform ergodic latent trajectory is pushed forward by a conditional flow matching map to produce asymptotically ergodic trajectories matching any target density with provable coverage bounds.
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TransSplat: Unbalanced Semantic Transport for Language-Driven 3DGS Editing
TransSplat uses unbalanced semantic transport to match edited 2D evidence with 3D Gaussians and recover a shared 3D edit field, yielding better local accuracy and structural consistency than prior view-consistency methods.