Wasserstein Lagrangian Mechanics formalizes second-order dynamics in Wasserstein space and provides an algorithm to learn them from observed marginals without specifying the Lagrangian, outperforming gradient flows on various dynamics.
arXiv preprint arXiv:2410.07550 , year=
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PAMF initializes flow matching with missingness-type priors and shares encoder weights between imputation and classification to improve multimodal time-series prediction under incomplete observations.
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A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots
Wasserstein Lagrangian Mechanics formalizes second-order dynamics in Wasserstein space and provides an algorithm to learn them from observed marginals without specifying the Lagrangian, outperforming gradient flows on various dynamics.
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PAMF: Prior-Aware Multimodal Fusion for Incomplete Time Series Data
PAMF initializes flow matching with missingness-type priors and shares encoder weights between imputation and classification to improve multimodal time-series prediction under incomplete observations.