SYSTEM AND METHOD FOR MACHINE LEARNING ARCHITECTURE WITH ADAPTIVE IMPORTANCE SAMPLING WITH NORMALIZING FLOWS

摘要

A system for computational estimation sampling from non-trivial probability distributions. The system comprises a processor, operating in conjunction with computer memory. The processor is configured to conduct importance sampling using normalizing flows where a base distribution has a set of parameters that can be adjusted to account for heavy-tailed distributions.