A finite-sampling neural architecture is dense in the Hilbert space of square-integrable predictable processes and attains best-N-term chaoslet rates for compressible or Malliavin-regular processes.
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NeuralChaos: Optimal Adapted Approximation of Square Integrable Predictable Processes
A finite-sampling neural architecture is dense in the Hilbert space of square-integrable predictable processes and attains best-N-term chaoslet rates for compressible or Malliavin-regular processes.