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:
Feb. 21, 2023

Filed:

Mar. 14, 2018
Applicant:

Perceive Corporation, San Jose, CA (US);

Inventors:

Eric A. Sather, Palo Alto, CA (US);

Steven L. Teig, Menlo Park, CA (US);

Andrew C. Mihal, San Jose, CA (US);

Assignee:

PERCEIVE CORPORATION, San Jose, CA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2006.01); G06F 17/18 (2006.01); G06N 3/04 (2006.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06F 17/18 (2013.01); G06N 3/04 (2013.01);
Abstract

Some embodiments provide a method for training a machine-trained (MT) network that processes input data using network parameters. The method maps input instances to output values by propagating the instances through the network. The input instances include instances for each of multiple categories. For a particular instance selected as an anchor instance, the method identifies each instance in a different category as a negative instance. The method calculates, for each negative instance of the anchor, a surprise function that probabilistically measures a surprise of finding an output value for an instance in the same category as the anchor that is a greater distance from the output value for the anchor instance than output value for the negative instance. The method calculates a loss function that emphasizes a maximum surprise calculated for the anchor. The method trains the network parameters using the calculated loss function value to minimize the maximum surprise.


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