Systems, methods and circuits for learning of relation-based networks
First Claim
1. A system for computations to learn the relationships and patterns in a set of observed objects, the system comprising:
- circuitry configured toimplement parallel and sequential implementation versions of an algorithm for search and sampling of structures; and
perform two dimensional encoding of ordering between the variables to facilitate efficient computations, fixed point representation of values for efficient computation.
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Accused Products
Abstract
Circuits, devices and methods for processing learning networks are implemented using a variety of methods and devices. One example involves a circuit-implemented method to identify a relationship of objects in a set of objects. Local scores are generated for the object and possible parents. The local scores indicate relationship strength between object and parent. The results are stored in a memory. A state-machine circuit is used to perform sampling and searching of the parent sets for each data node. The local scores are used to encode orderings of the parent. An algorithm is executed that uses the encoded possible orderings and a random variable to generate and score a current order and a proposed order of the possible parent sets. The proposed orders are accepted or rejected based on probability rules applied to the scores for the current and proposed orders. Structures are sampled to assess a Bayesian-based relationship.
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Citations
22 Claims
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1. A system for computations to learn the relationships and patterns in a set of observed objects, the system comprising:
circuitry configured to implement parallel and sequential implementation versions of an algorithm for search and sampling of structures; and perform two dimensional encoding of ordering between the variables to facilitate efficient computations, fixed point representation of values for efficient computation. - View Dependent Claims (2, 3, 4)
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5. A circuit-implemented method to identify at least one pattern, or relationship, of objects in a set of objects, the method comprising:
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pre-processing the set of objects by operating a logic circuit to generate local scores for each object and possible parent sets of that object, wherein the local scores are indicative of the strength of the relationship between each object and a parent set, and storing the local scores and the possible parent sets in a memory circuit that is configured and arranged to provide, for each of the data nodes, access to each of the possible parent sets and the local scores; after pre-processing the set of objects, while accessing the stored local scores and possible parent sets, using a state-machine circuit to perform sampling and searching of the parent sets for each data node, including using the local scores to encode possible orderings of the parent sets, executing an algorithm, that uses the encoded possible orderings and a random variable, for generating and scoring a current order and a proposed order of the possible parent sets, and choosing to accept or reject the proposed orders based on a set of probability rules applied to the scores for the current and proposed orders; and sampling structures derived from the accepted proposed orders to assess and, indicating therefrom, a Bayesian-based relationship. - View Dependent Claims (6, 7, 8, 9, 10, 11, 12, 13)
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14. A system to identify at least one pattern, or relationship, of data nodes in a set of data nodes, the system comprising:
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a pre-processing logic circuit configured and arranged to generate local scores for each object and possible parent sets of that object, wherein the local scores are indicative of the strength the relationship between each object and a parent set, and store the local scores and the possible parent sets in a memory circuit that is configured and arranged to provide, for each of the data nodes, access to each of the possible parent sets and the local scores; a state-machine circuit configured and arranged to, after pre-processing the set of objects and while accessing the stored local scores and possible parent sets, perform sampling and searching of the parent sets for each data node including using the local scores to encode possible orderings of the parent sets, executing an algorithm, that uses the encoded possible orderings and a random variable, for generating and scoring a current order and a proposed order of the possible parent sets, and choosing to accept or reject the proposed orders based on a set of probability rules applied to the scores for the current and proposed orders; and a processing circuit configured and arranged to sample structures derived from the accepted proposed orders to assess and, indicating therefrom, a Bayesian-based relationship. - View Dependent Claims (15, 16, 17, 18, 19, 20, 21, 22)
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Specification