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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A generative framework using latent heteroscedastic Gaussian process approximated via Hilbert space methods plus optimal transport to model population trends and infer trajectories in temporal scRNA-seq data.
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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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Modeling Temporal scRNA-seq Data with Latent Gaussian Process and Optimal Transport
A generative framework using latent heteroscedastic Gaussian process approximated via Hilbert space methods plus optimal transport to model population trends and infer trajectories in temporal scRNA-seq data.