Neural network categorization accuracy with categorical graph neural networks

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United States of America

PATENT NO 11960573
SERIAL NO

17981822

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Abstract

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Neural network-based categorization can be improved by incorporating graph neural networks that operate on a graph representing the taxonomy of the categories into which a given input is to be categorized by the neural network based-categorization. The output of a graph neural network, operating on a graph representing the taxonomy of categories, can be combined with the output of a neural network operating upon the input to be categorized, such as through an interaction of multidimensional output data, such as a dot product of output vectors. In such a manner, information conveying the explicit relationships between categories, as defined by the taxonomy, can be incorporated into the categorization. To recapture information, incorporate new information, or reemphasize information a second neural network can also operate upon the input to be categorized, with the output of such a second neural network being merged with the output of the interaction.

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Patent Owner(s)

Patent OwnerAddress
MICROSOFT TECHNOLOGY LICENSING LLCONE MICROSOFT WAY REDMOND WA 98052

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Inventor(s)

Inventor Name Address # of filed Patents Total Citations
Chang, Keng-Hao San Jose, US 31 200
Du, Tianchuan Sunnyvale, US 2 1
Liu, Paul Palo Alto, US 28 149
Zhang, Ruofei Mountain View, US 59 1052

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