| CPC G06N 3/08 (2013.01) [G06F 16/904 (2019.01); G06F 18/2155 (2023.01); H03M 7/60 (2013.01); G06N 3/043 (2023.01); G06N 3/086 (2013.01); G06N 3/10 (2013.01); G06N 3/105 (2013.01)] | 20 Claims |
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1. A computer-implemented method for improving compression of predictive models, the computer-implemented method comprising:
generating an unlabeled simulated data set by expanding an initial data set, wherein the initial data set includes a first plurality of fact sets and wherein the unlabeled simulated data set includes a second plurality of fact sets;
generating a labeled data set, at least by predicting the unlabeled simulated data set using a complex model to output a plurality of labels, wherein the labeled data set includes the second plurality of fact sets and the plurality of labels, and wherein each fact set of the second plurality of fact sets corresponds to a respective one of the plurality of labels; and
training, using the labeled data set, a neural network model associated with a plurality of training parameters, wherein training the neural network model includes:
(i) generating a plurality of intermediate predictions, at least by predicting the second plurality of fact sets using the neural network model,
(ii) comparing the plurality of labels to the plurality of intermediate predictions to produce a measure of accuracy, and
(iii) modifying, based on the measure of accuracy, at least one of the plurality of training parameters of the neural network model.
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