Invention Grant
- Patent Title: Data-dependent node-to-node knowledge sharing by regularization in deep learning
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Application No.: US18742404Application Date: 2024-06-13
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Publication No.: US12136027B2Publication Date: 2024-11-05
- Inventor: James K. Baker , Bradley J. Baker
- Applicant: D5AI LLC
- Applicant Address: US FL Maitland
- Assignee: D5AI LLC
- Current Assignee: D5AI LLC
- Current Assignee Address: US FL Maitland
- Agency: K&L Gates LLP
- Main IPC: G06N3/04
- IPC: G06N3/04 ; G06F18/40

Abstract:
Data-dependent node-to-node knowledge sharing to increase the interpretability of the activation pattern of one or more nodes in a neural network, is implemented by a set of knowledge sharing links. Each link may comprise a knowledge providing node or other source P and a knowledge receiving node R. A knowledge sharing link can impose a node-specific regularization on the knowledge receiving node R to help guide the knowledge receiving node R to have an activation pattern that is more easily interpreted. The specification and training of the knowledge sharing links may be controlled by a cooperative human-AI learning supervisor system in which a human and an artificial intelligence system work cooperatively to improve the interpretability and performance of the client system.
Public/Granted literature
- US20240330644A1 DATA-DEPENDENT NODE-TO-NODE KNOWLEDGE SHARING BY REGULARIZATION IN DEEP LEARNING Public/Granted day:2024-10-03
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