Invention Grant
- Patent Title: Method of and server for training a machine learning algorithm for estimating uncertainty of a sequence of models
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Application No.: US16730008Application Date: 2019-12-30
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Publication No.: US11562203B2Publication Date: 2023-01-24
- Inventor: Gabrielle Gauthier Melançon , Waseem Gharbieh , Iman Malik , William Xavier Snelgrove
- Applicant: ELEMENT AI Inc.
- Applicant Address: CA Montreal
- Assignee: ELEMENT AI Inc.
- Current Assignee: ELEMENT AI Inc.
- Current Assignee Address: CA Montreal
- Agency: Fasken Martineau Dumoulin LLP
- Agent Serge Lapointe
- Main IPC: G06N3/04
- IPC: G06N3/04 ; G06V10/40 ; G06V10/82 ; G06V30/19 ; G06N20/20 ; G06N5/00 ; G06V30/10

Abstract:
There is provided a method and server for estimating an uncertainty parameter of a sequence of computer-implemented models comprising at least one machine learning algorithm (MLA). A set of labelled digital documents is received, which is to be processed by the sequence of models. For a given model of the sequence of models, at least one of a respective set of input features, a respective set of model-specific features and a respective set of output features are received. The set of predictions output by the sequence of models is received. A second MLA is trained to estimate uncertainty of the sequence of models based on the set of labelled digital documents, and the at least one of the respective set of input features, the respective set of model-specific features, the respective set of output features, and the set of predictions.
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