Training and sampling in static scalar energy generative models are two instances of the same Lyapunov-driven density transport dynamics on Wasserstein space, differing only by initial condition, which yields a finite stopping criterion for Langevin sampling and additive composition rules that keep
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StrEBM applies source-wise Gaussian-process-inspired energies with learnable length-scales to jointly optimize latent trajectories and observation mappings for recovering components from linear and nonlinear mixtures.
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Energy Generative Modeling: A Lyapunov-based Energy Matching Perspective
Training and sampling in static scalar energy generative models are two instances of the same Lyapunov-driven density transport dynamics on Wasserstein space, differing only by initial condition, which yields a finite stopping criterion for Langevin sampling and additive composition rules that keep
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StrEBM: A Structured Latent Energy-Based Model for Blind Source Separation
StrEBM applies source-wise Gaussian-process-inspired energies with learnable length-scales to jointly optimize latent trajectories and observation mappings for recovering components from linear and nonlinear mixtures.