Self-organizing partially ordered networks
AssigneeD5Ai LLC
InventorsJames K. BAKER, Bradley J. BAKER
Life of this patent
Estimating the term…
Abstract
A system and method for controlling a nodal network. The method includes estimating an effect on the objective caused by the existence or non-existence of a direct connection between a pair of nodes and changing a structure of the nodal network based at least in part on the estimate of the effect. A nodal network includes a strict partially ordered set, a weighted directed acyclic graph, an artificial neural network, and/or a layered feed-forward neural network.
Claims81
A computer-implemented method for controlling an artificial neural network comprising a first node and a second node, the first and second nodes comprising activation functions that are evaluatable on a dataset according to an objective defined by an objective function, the method comprising:
- estimating, by a computer system, an effect on the objective caused by the existence or non-existence of a direct connection between the first node and the second node based at least in part on activation values of the first node and estimates of partial derivatives of the objective with respect the activation of the second node;
- and
- changing, by the computer system, a structure of the neural network based at least in part on the estimate of the effect.
A computer system for controlling a nodal an artificial neural network, the computer system comprising:
- a processor;
- and
- a memory coupled to the processor, the memory storing:
- the neural network comprising a first node and a second node, the first and second nodes comprising activation functions that are evaluatable on a dataset according to an objective defined by an objective function;
- and
- instructions that, when executed by the processor, cause the computer system to:
- estimate an effect on the objective caused by the existence or non-existence of a direct connection between the first node and the second node based at least in part on activation values of the first node and estimates of partial derivatives of the objective with respect the activation of the second node;
- and
- change a structure of the neural network based at least in part on whether the direct connection between the first node and the second node exists.
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Dependencies are inferred from the claim text.