System and method for improving compression of predictive models
AssigneeQuanata, LLC
InventorKenneth J. Sanchez
Life of this patent
Estimating the term…
Abstract
A computer-implemented method for improving compression of predictive models can include providing a labeled data set. The computer-implemented method also can include training, using the labeled data set, a neural network model associated with one or more training parameters to create a trained neural network model. Other embodiments are disclosed.
Claims20
A computer-implemented method for improving compression of predictive models, the computer-implemented method comprising:
- providing a labeled data set, wherein the labeled data set comprises:
- one or more first fact sets;
- and
- one or more labels;
- and
- training, using the labeled data set, a neural network model associated with one or more training parameters to create a trained neural network model, wherein training the neural network model comprises:
- (i) generating one or more intermediate predictions, by at least predicting the one or more first fact sets using the neural network model;
- (ii) comparing the one or more labels to the one or more intermediate predictions to produce a measure of accuracy for the one or more intermediate predictions;
- and
- (iii) modifying, based on the measure of accuracy, at least one of the one or more training parameters of the neural network model.
A system comprising:
- one or more processors;
- and
- one or more non-transitory computer-readable storage media storing computing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
- providing a labeled data set, wherein the labeled data set comprises:
- one or more first fact sets;
- and
- one or more labels;
- and
- training, using the labeled data set, a neural network model associated with one or more training parameters to create a trained neural network model, wherein training the neural network model comprises:
- (i) generating one or more intermediate predictions, by at least predicting the one or more first fact sets using the neural network model;
- (ii) comparing the one or more labels to the one or more intermediate predictions to produce a measure of accuracy for the one or more intermediate predictions;
- and
- (iii) modifying, based on the measure of accuracy, at least one of the one or more training parameters of the neural network model.
A non-transitory computer readable storage medium storing one or more computing instructions that, when run on one or more processors, cause the one or more processors to perform operations comprising:
- providing a labeled data set, wherein the labeled data set comprises:
- one or more first fact sets;
- and
- one or more labels;
- and
- training, using the labeled data set, a neural network model associated with one or more training parameters to create a trained neural network model, wherein training the neural network model comprises:
- (i) generating one or more intermediate predictions, by at least predicting the one or more first fact sets using the neural network model;
- (ii) comparing the one or more labels to the one or more intermediate predictions to produce a measure of accuracy for the one or more intermediate predictions;
- and
- (iii) modifying, based on the measure of accuracy, at least one of the one or more training parameters of the neural network model.
Dependencies are inferred from the claim text.