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

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

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 parame...

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Bibliographische Detailangaben
Hauptverfasser: LAI, ZHEXIN, BRUBAKER, MARCUS A, KHOSHAMAN, AMIR H
Format: Patent
Sprache:eng ; fre
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
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Beschreibung
Zusammenfassung: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.