The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Nov. 14, 2023

Filed:

Oct. 29, 2020
Applicant:

Nvidia Corporation, Santa Clara, CA (US);

Inventors:

Thomas Müller, Rheinfelden, DE;

Fabrice Pierre Armand Rousselle, Ostermundigen, CH;

Alexander Georg Keller, Berlin, DE;

Jan Novák, Zürich, CH;

Assignee:

NVIDIA Corporation, Santa Clara, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 30/27 (2020.01); G06T 15/06 (2011.01); G06F 17/11 (2006.01); G06T 15/50 (2011.01); G06N 3/045 (2023.01); G06F 111/10 (2020.01);
U.S. Cl.
CPC ...
G06F 30/27 (2020.01); G06F 17/11 (2013.01); G06N 3/045 (2023.01); G06T 15/06 (2013.01); G06T 15/506 (2013.01); G06F 2111/10 (2020.01);
Abstract

Monte Carlo and quasi-Monte Carlo integration are simple numerical recipes for solving complicated integration problems, such as valuating financial derivatives or synthesizing photorealistic images by light transport simulation. A drawback of a straightforward application of (quasi-)Monte Carlo integration is the relatively slow convergence rate that manifests as high error of Monte Carlo estimators. Neural control variates may be used to reduce error in parametric (quasi-)Monte Carlo integration—providing more accurate solutions in less time. A neural network system has sufficient approximation power for estimating integrals and is efficient to evaluate. The efficiency results from the use of a first neural network that infers the integral of the control variate and using normalizing flows to model a shape of the control variate.


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