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:

May. 18, 2020
Applicant:

Microsoft Technology Licensing, Llc, Redmond, WA (US);

Inventors:

Subhabrata Mukherjee, Seattle, WA (US);

Guoqing Zheng, Redmond, WA (US);

Ahmed Awadalla, Redmond, WA (US);

Milad Shokouhi, Seattle, WA (US);

Susan Theresa Dumais, Kirkland, WA (US);

Kai Shu, Mesa, AZ (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/82 (2022.01); G06N 3/08 (2023.01); G06N 3/04 (2023.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06N 3/04 (2013.01); G06V 10/82 (2022.01);
Abstract

This document relates to training of machine learning models. One example method involves providing a machine learning model having a first classification layer, a second classification layer, and an encoder that feeds into the first classification layer and the second classification layer. The example method also involves obtaining first training examples having explicit labels and second training examples having inferred labels. The inferred labels are based at least on actions associated with the second training examples. The example method also involves training the machine learning model using the first training examples and the second training examples using a training objective that considers first training loss of the first classification layer for the explicit labels and second training loss of the second classification layer for the inferred labels. The method also involves outputting a trained machine learning model having the encoder and the first classification layer.


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