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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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.