Invention Grant
- Patent Title: Joint learning from explicit and inferred labels
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Application No.: US18376615Application Date: 2023-10-04
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Publication No.: US12073326B2Publication Date: 2024-08-27
- Inventor: Subhabrata Mukherjee , Guoqing Zheng , Ahmed Awadalla , Milad Shokouhi , Susan Theresa Dumais , Kai Shu
- Applicant: Microsoft Technology Licensing, LLC
- Applicant Address: US WA Redmond
- Assignee: Microsoft Technology Licensing, LLC
- Current Assignee: Microsoft Technology Licensing, LLC
- Current Assignee Address: US WA Redmond
- Agency: Rainier Patents, P.S.
- The original application number of the division: US16876931 2020.05.18
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06N3/04 ; G06V10/82 ; G06F16/176

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.
Public/Granted literature
- US20240046087A1 JOINT LEARNING FROM EXPLICIT AND INFERRED LABELS Public/Granted day:2024-02-08
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