Method and apparatus for improving resilience in customized program learning network computational environments
AssigneeGeorges Harik
InventorGeorges Harik
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Abstract
An apparatus and a method are provided for learning a program with a large number of parameters. In one embodiment, a method not only distorts the input values, but also distorts some of the parameters in the program model. Such an approach not only forces the learned program to acquire parameter values to predict missing or desired data, but also to correct errors in the input data and the program parameters themselves, thereby rendering the learned program more resilient to overfitting and falling into local optima.
Claims21
In a computational environment, a method for learning a program that is characterized by a number of parameters, the computing environment allowing the program to receive an input vector, an expected output vector and values of the parameters, the method comprising of:
- choosing an input distortion percentage and a parameter distortion percentage each having a value between zero and a respective predetermined maximum and;
- blanking out or randomizing the input distortion percentage of values in the input vector;
- blanking out or randomizing the parameter distortion percentage of the values of the parameters of the program;
- and
- applying a learning technique to the program, using the expected output vector, the distorted input vector and distorted parameters.
An apparatus for learning a program that is characterized by a number of parameters, the apparatus receiving an input vector, an expected output vector, and values of the parameters, the apparatus comprising:
- an input randomizing module;
- a parameter randomizing module;
- a parameter updater and
- an expected output module, wherein the apparatus carries out a method comprising:
- choosing an input distortion percentage and a parameter distortion percentage each having a value between zero and a respective predetermined maximum and;
- in the input randomizing module, blanking out or randomizing the input distortion percentage of values in the input vector;
- in the parameter randomizing module, blanking out or randomizing the parameter distortion percentage of the values of the parameters of the program;
- and
- applying a learning technique to the program, using the expected output vector, the distorted input vector and distorted parameters.
Dependencies are inferred from the claim text.