CT-OT Flow estimates continuous-time dynamics from discrete temporal snapshots by using partial optimal transport to align intervals and kernel smoothing to reconstruct distributions for ODE/SDE training.
Light Schrödinger bridge
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2025 2verdicts
UNVERDICTED 2representative citing papers
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.
citing papers explorer
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CT-OT Flow: Estimating Continuous-Time Dynamics from Discrete Temporal Snapshots
CT-OT Flow estimates continuous-time dynamics from discrete temporal snapshots by using partial optimal transport to align intervals and kernel smoothing to reconstruct distributions for ODE/SDE training.
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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.