Infinite-dimensional GAL can learn the invariant distribution of sufficiently chaotic dynamical systems from a single deterministic time series with explicit JS divergence convergence rates.
Ehrhardt, Hanno Gottschalk, and Tobias J
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Proves PAC consistency and explicit convergence rates for learned transport integrated (LtI) quadrature using neural ODE flows for general targets and empirical quantile maps for product targets.
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Generative Adversarial Learning from Deterministic Processes
Infinite-dimensional GAL can learn the invariant distribution of sufficiently chaotic dynamical systems from a single deterministic time series with explicit JS divergence convergence rates.
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Consistency of Learned Sparse Grid Quadrature Rules using NeuralODEs
Proves PAC consistency and explicit convergence rates for learned transport integrated (LtI) quadrature using neural ODE flows for general targets and empirical quantile maps for product targets.