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US Patent 11681922 Performing inference and training using sparse neural network

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Patent
Patent
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Patent attributes

Patent Applicant
Numenta
Numenta
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Current Assignee
Numenta
Numenta
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
116819220
Date of Patent
June 20, 2023
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Patent Application Number
166969910
Date Filed
November 26, 2019
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Patent Citations
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US Patent 7613675 Hierarchical computing modules for performing recognition using spatial distance and temporal sequences
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US Patent 7088693 Adaptive diversity combining for Wide Band Code Division Multiple Access (W-CDMA) based on iterative channel estimation
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US Patent 7251637 Context vector generation and retrieval
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US Patent 7308134 Pattern recognition with hierarchical networks
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US Patent 8219507 Hierarchical temporal memory system with enhanced inference capability
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US Patent 8285667 Sequence learning in a hierarchical temporal memory based system
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US Patent 8290886 Trainable hierarchical memory system and method
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US Patent 11049013 Encoding of weight values stored on neural network inference circuit
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Patent Primary Examiner
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Charles T Shedrick
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CPC Code
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G06N 3/0454
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G06N 3/084
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G06N 3/04
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G06N 3/0445
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An inference system trains and performs inference using a sparse neural network. The sparse neural network may include one or more layers, and each layer may be associated with a set of sparse weights that represent sparse connections between nodes of a layer and nodes of a previous layer. A layer output may be generated by applying the set of sparse weights associated with the layer to the layer output of a previous layer. Moreover, the one or more layers of the sparse neural network may generate sparse layer outputs. By using sparse representations of weights and layer outputs, robustness and stability of the neural network can be significantly improved, while maintaining competitive accuracy.

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