Minimizing Global Error in an Artificial Neural Network
First Claim
1. A method comprising:
- storing an artificial neural network model that is configured to predict one or more outputs based at least in part on one or more inputs, wherein the artificial neural network model comprises an input layer, one or more intermediate layers, and an output layer; and
minimizing an approximate global error in the artificial neural network model at least in part by causing evaluation of a mixed integer linear program that determines one or more weights between two or more artificial neurons in the artificial neural network model, wherein the mixed integer linear program accounts for one or more piecewise linear activation functions for one or more artificial neurons in the artificial neural network model;
wherein the method is performed by one or more computing devices.
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Abstract
Computer systems, machine-implemented methods, and stored instructions are provided for minimizing an approximate global error in an artificial neural network that is configured to predict model outputs based at least in part on one or more model inputs. A model manager stores the artificial neural network model. The model manager may then minimize an approximate global error in the artificial neural network model at least in part by causing evaluation of a mixed integer linear program that determines weights between artificial neurons in the artificial neural network model. The mixed integer linear program accounts for piecewise linear activation functions for artificial neurons in the artificial neural network model. The mixed integer linear program comprises a functional expression of a difference between actual data and modeled data, and a set of one or more constraints that reference variables in the functional expression.
19 Citations
20 Claims
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1. A method comprising:
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storing an artificial neural network model that is configured to predict one or more outputs based at least in part on one or more inputs, wherein the artificial neural network model comprises an input layer, one or more intermediate layers, and an output layer; and minimizing an approximate global error in the artificial neural network model at least in part by causing evaluation of a mixed integer linear program that determines one or more weights between two or more artificial neurons in the artificial neural network model, wherein the mixed integer linear program accounts for one or more piecewise linear activation functions for one or more artificial neurons in the artificial neural network model; wherein the method is performed by one or more computing devices. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. One or more non-transitory storage media storing instructions which, when executed by one or more processors, cause:
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storing an artificial neural network model that is configured to predict one or more outputs based at least in part on one or more inputs, wherein the artificial neural network model comprises an input layer, one or more intermediate layers, and an output layer; and minimizing an approximate global error in the artificial neural network model at least in part by causing evaluation of a mixed integer linear program that determines one or more weights between two or more artificial neurons in the artificial neural network model, wherein the mixed integer linear program accounts for one or more piecewise linear activation functions for one or more artificial neurons in the artificial neural network model. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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Specification