- Patent Title: Game-theoretic frameworks for deep neural network rationalization
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Application No.: US16658122Application Date: 2019-10-20
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Publication No.: US11657271B2Publication Date: 2023-05-23
- Inventor: Shiyu Chang , Mo Yu , Yang Zhang , Tommi S. Jaakkola
- Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION , MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- Applicant Address: US NY Armonk
- Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION,MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- Current Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION,MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- Current Assignee Address: US NY Armonk; US MA Cambridge
- Agency: Intelletek Law Group, PLLC
- Agent Gabriel Daniel, Esq.
- Main IPC: G06N3/04
- IPC: G06N3/04 ; G06N3/08 ; G06K9/62

Abstract:
A method and system of determining an output label rationale are provided. A first generator receives a first class of data and selects one or more input features from the first class of data. A first predictor receives the one or more selected input features from the first generator and predicts a first output label. A second generator receives a second class of data and selects one or more input features from the second class of data. A second predictor receives the one or more selected input features from the second generator and predicts a second output label. A discriminator receives the first and second output labels and determines whether the selected one or more input features from the first class of data or the selected features of the one or more input features from the second class of data, more accurately represents the first output label.
Public/Granted literature
- US20210117772A1 Game-Theoretic Frameworks for Deep Neural Network Rationalization Public/Granted day:2021-04-22
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