SUPERVISION BASED GROUPING OF PATTERNS IN HIERARCHICAL TEMPORAL MEMORY (HTM)
First Claim
1. An Hierarchical Temporal Memory (HTM) network system comprising a top node and a supervised learning node, the top node configured to generate an output representing an object or a state of an object responsive to receiving sample input data in an inference mode, comprising:
- a supervised learning node configured to receive a training input data in a training mode and group patterns in the training input data responsive to receiving a supervision signal representing a correct category of the object or the state of the object for the training input data, and the supervised learning node in the inference mode subsequent to the training mode generating first information about patterns in sample input data corresponding to the patterns grouped in the training mode, the top node in the inference mode generating the output based on the first information generated at the supervised learning node.
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Abstract
A HTM network that uses supervision signals such as indexes for correct categories of the input patterns to group the co-occurrences detected in the node. In the training mode, the supervised learning node receives the supervision signals in addition to the indexes or distributions from children nodes. The supervision signal is then used to assign the co-occurrences into groups. The groups include unique groups and nonunique groups. The co-occurrences in the unique group appear only when the input data represent certain category but not others. The nonunique groups include patterns that are shared by one or more categories. In an inference mode, the supervised learning node generates distributions over the groups created in the training mode. A top node of the HTM network generates an output based on the distributions generated by the supervised learning node.
100 Citations
21 Claims
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1. An Hierarchical Temporal Memory (HTM) network system comprising a top node and a supervised learning node, the top node configured to generate an output representing an object or a state of an object responsive to receiving sample input data in an inference mode, comprising:
a supervised learning node configured to receive a training input data in a training mode and group patterns in the training input data responsive to receiving a supervision signal representing a correct category of the object or the state of the object for the training input data, and the supervised learning node in the inference mode subsequent to the training mode generating first information about patterns in sample input data corresponding to the patterns grouped in the training mode, the top node in the inference mode generating the output based on the first information generated at the supervised learning node. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A computer-implemented method of determining an object or a state of an object in a supervised learning node in a Hierarchical Temporal Memory (HTM) network system, the HTM network comprising a top node, comprising:
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grouping patterns in training input data responsive to receiving training input data in a training mode and a supervision signal representing a correct category of the object or the state of the object for the training input data; generating first information about patterns in sample input data corresponding to the patterns grouped in the training mode in an inference mode subsequent to the training mode; and sending the first information to the top node in the inference mode for generating an output representing the object or the state of the object corresponding to the input data based on the first information. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A computer-readable storage medium storing instructions for determining an object or a state of an object in a supervised learning node in a Hierarchical Temporal Memory (HTM) network system, the instructions when executed by a processor causes the processor to
group patterns in training input data responsive to receiving training input data in a training mode and a supervision signal representing a correct category of the object or the state of the object for the training input data; -
generate first information about patterns in sample input data corresponding to the patterns grouped in the training mode in an inference mode subsequent to the training mode; and send the first information to the top node in the inference mode for generating an output representing the object or the state of the object corresponding to the input data based on the first information. - View Dependent Claims (16, 17, 18, 19, 20, 21)
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Specification