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:
Sep. 15, 2020

Filed:

Dec. 24, 2019
Applicant:

Sas Institute Inc., Cary, NC (US);

Inventors:

Rui Shi, Bethlehem, PA (US);

Seyedalireza Yektamaram, Cary, NC (US);

Yan Xu, Cary, NC (US);

Assignee:

SAS INSTITUTE INC., Cary, NC (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/06 (2006.01); G06N 99/00 (2019.01); G06N 3/063 (2006.01); A61B 5/00 (2006.01); G06N 20/00 (2019.01); G06F 40/20 (2020.01); G06N 3/04 (2006.01);
U.S. Cl.
CPC ...
G06N 20/00 (2019.01); G06F 40/20 (2020.01); G06N 3/04 (2013.01);
Abstract

Machine-learning models (MLM) can be configured more rapidly using some examples described herein. For example, a MLM can be configured by executing an iterative process, where each iteration includes a series of operations. The series of operations can include determining a current weight value for the current iteration, determining a current gradient direction for the current iteration based on the current weight value, and determining a current learning rate for the current iteration based on the current gradient direction. The operations can also include determining a current multistage momentum value for the current iteration. A next weight value for a next iteration can then be determined based on (i) the current weight value, (ii) the current gradient direction, (iii) the current learning rate, and (iv) the current multistage momentum value. The next weight value may also be determined based on a predefined learning rate that was preset, in some examples.


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