SYSTEM AND METHOD FOR IMPROVING COMPRESSION OF PREDICTIVE MODELS
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.
1 . 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.
2 . The computer-implemented method of claim 1 wherein (i), (ii), or (iii) are iteratively repeated until the measure of accuracy is within a predetermined threshold.
3 . The computer-implemented method of claim 1 , further comprising:
providing an unlabeled simulated data set by expanding an initial data set, wherein:
the unlabeled simulated data set comprises the one or more first fact sets; and
the initial data set comprises one or more second fact sets.
4 . The computer-implemented method of claim 3 , wherein:
the one or more first fact sets and the one or more second fact sets comprise one or more fact types; and
providing the unlabeled simulated data set comprises providing the unlabeled simulated data set such that a distribution of the one or more fact types of the one or more first fact sets is skewed as compared to a distribution of the one or more fact types of the one or more second fact sets.
5 . The computer-implemented method of claim 1 , further comprising:
receiving, from an electronic database, a definition of the one or more training parameters of the neural network model.
6 . The computer-implemented method of claim 1 , wherein generating the one or more intermediate predictions comprises:
dividing the one or more first fact sets into one or more fact subsets;
receiving, at one or more networked computing devices, the one or more fact subsets;
generating, by the one or more networked computing devices, a respective intermediate prediction; and
receiving, at the one or more networked computing devices, the respective intermediate prediction.
7 . The computer-implemented method of claim 1 , further comprising:
transmitting, to a computing device, the neural network model, as trained; and
providing an unlabeled new data set based upon data collected by the computing device, wherein the unlabeled new data set comprises one or more new fact sets.
8 . The computer-implemented method of claim 7 , further comprising:
generating one or more device predictions, by at least predicting the unlabeled new data set using the neural network model, as trained.
9 . The computer-implemented method of claim 1 , further comprising:
sending, to a computing device, the neural network model, as trained, to enable the computing device to analyze one or more unlabeled new data sets using the neural network model, as trained.
10 . The computer-implemented method of claim 9 , wherein the computing device is a mobile electronic device of a user.
11 . 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.
12 . The system of claim 11 , wherein (i), (ii), or (iii) are iteratively repeated until the measure of accuracy is within a predetermined threshold.
13 . The system of claim 11 , wherein the computing instructions, when executed on the one or more processors, further cause the one or more processors to perform operations comprising:
providing an unlabeled simulated data set by expanding an initial data set, wherein:
the unlabeled simulated data set comprises the one or more first fact sets; and
the initial data set comprises one or more second fact sets.
14 . The system of claim 13 , wherein:
the one or more first fact sets and the one or more second fact sets comprise one or more fact types; and
providing the unlabeled simulated data set comprises providing the unlabeled simulated data set such that a distribution of the one or more fact types of the one or more first fact sets is skewed as compared to a distribution of the one or more fact types of the one or more second fact sets.
15 . The system of claim 11 , wherein the computing instructions, when executed on the one or more processors, further cause the one or more processors to perform operations comprising:
receiving, from an electronic database, a definition of the one or more training parameters of the neural network model.
16 . The system of claim 11 , wherein generating the one or more intermediate predictions comprises:
dividing the one or more first fact sets into one or more fact subsets;
receiving, at one or more networked computing devices, the one or more fact subsets;
generating, by the one or more networked computing devices, a respective intermediate prediction; and
receiving, at the one or more networked computing devices, the respective intermediate prediction.
17 . The system of claim 11 , wherein the computing instructions, when executed on the one or more processors, further cause the one or more processors to perform operations comprising:
transmitting, to a computing device, the neural network model, as trained; and
providing an unlabeled new data set based upon data collected by the computing device, wherein the unlabeled new data set comprises one or more new fact sets.
18 . The system of claim 17 , wherein the computing instructions, when executed on the one or more processors, further cause the one or more processors to perform operations comprising:
generating one or more device predictions, by at least predicting the unlabeled new data set using the neural network model, as trained.
19 . The system of claim 11 , wherein the computing instructions, when executed on the one or more processors, further cause the one or more processors to perform operations comprising:
sending, to a mobile electronic device, the neural network model, as trained, to enable the mobile electronic device of a user to analyze one or more unlabeled new data sets using the neural network model, as trained.
20 . 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.