Invention Grant
- Patent Title: Weakly supervised multi-task learning for concept-based explainability
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Application No.: US17461217Application Date: 2021-08-30
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Publication No.: US11544471B2Publication Date: 2023-01-03
- Inventor: Catarina Garcia Belém , Vladimir Balayan , Pedro dos Santos Saleiro , Pedro Gustavo Santos Rodrigues Bizarro
- Applicant: Feedzai—Consultadoria e Inovação Tecnológica, S.A.
- Applicant Address: PT Coimbra
- Assignee: Feedzai—Consultadoria e Inovação Tecnológica, S.A.
- Current Assignee: Feedzai—Consultadoria e Inovação Tecnológica, S.A.
- Current Assignee Address: PT Coimbra
- Agency: Van Pelt, Yi & James LLP
- Priority: EP21193263 20210826,PT117425 20210826
- Main IPC: G06F40/30
- IPC: G06F40/30 ; G06F40/169 ; G06N3/08 ; G06N3/04 ; G06Q40/02 ; G06Q20/40

Abstract:
A labeling function associated with generating one or more semantic concepts is received. The received labeling function is used to automatically annotate an existing dataset with the one or more semantic concepts to generate an annotated noisy dataset. A reference dataset annotated with the one or more semantic concepts is received. A training dataset is prepared including by combining at least a portion of the reference dataset with at least a portion of the annotated noisy dataset. The training dataset is used to train a multi-task machine learning model configured to perform both a decision task to predict a decision result and an explanation task to predict a plurality of semantic concepts for explainability associated with the decision task.
Public/Granted literature
- US20220114345A1 WEAKLY SUPERVISED MULTI-TASK LEARNING FOR CONCEPT-BASED EXPLAINABILITY Public/Granted day:2022-04-14
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