Systems and methods for classifying data sets using corresponding neural networks

1. A system for classifying data sets by corresponding functions, comprising:

at least one processor; and

at least one non-transitory memory storing instructions that, when executed by the at least one processor cause the system to perform operations comprising:

obtaining a neural network associated with a characteristic of a first data set, the neural network being trained to generate synthetic data sets having at least the characteristic of the first data set;

selecting a test set of inputs for the neural network;

obtaining a corresponding set of outputs from the neural network, by applying the neural network to the test set of inputs, the neural network outputs comprising a second data set, the second data set comprising synthetic data;

estimating one or more corresponding functions that model the test set of inputs to the corresponding set of neural network outputs; and

indexing the estimated one or more functions to the first data set.

2. The system of claim 1 , wherein the operations further comprise storing the indexed functions and datasets in a function index.

3. The system of claim 2 , wherein the operations further comprise

receiving a search query indicating at least one search query function;

searching the function index based on the search query; and

returning, based on the search, the first data set.

4. The system of claim 3 , wherein searching the function index based on the search query comprises matching the search query function to a function stored in the function index.

5. The system of claim 4 , wherein matching the search query comprises finding a fuzzy match between the search query function and a function stored in the function index.

6. The system of claim 3 , wherein searching the function index based on the search query comprises:

clustering one or more functions of the function index to form a plurality of function clusters; and

matching the search query function to a first cluster of the plurality of clusters.

7. The system of claim 6 , wherein matching the search query function to a first cluster comprises:

generating a score for the first cluster indicating a degree of belongingness of the search query function to the first cluster; and

determining that the score exceeds a predetermined threshold.

8. A computer-implemented method for classifying data sets by corresponding functions comprising:

obtaining a neural network associated with a characteristic of a first data set, the neural network being trained to generate synthetic data sets having at least the characteristic of the first data set;

selecting a test set of inputs for the neural network;

obtaining a corresponding set of outputs, from the neural network, by applying the neural network to the test set of inputs, the neural network outputs comprising a second data set, the second data set comprising synthetic data;

estimating one or more corresponding functions that model the test set of inputs to the corresponding set of neural network outputs; and

indexing the estimated one or more functions to the first data set.

9. The method of claim 8 , wherein the first data set comprises at one of text files or image files.

10. The method of claim 8 , further comprising generating the test set of inputs using a stochastic algorithm.

11. The method of claim 8 , wherein the one or more functions comprise local approximations of a global function representing the neural network.

12. The method of claim 8 , wherein the one or more functions comprise polynomial approximations.

13. The method of claim 8 , wherein the one or more functions comprise harmonic approximations.

14. The method of claim 8 , further comprising wherein the neural network comprises at least one of a convolutional neural network, a recurrent neural network, an auto-encoder, a variational auto-encoder, or a generative adversarial network.

15. A system for classifying data sets by corresponding functions, comprising:

at least one processor; and

at least one non-transitory memory storing instructions that, when executed by the at least one processor cause the system to perform operations comprising:

obtaining a first data set;

training, using the first data set, a neural network to generate synthetic data sets having a characteristic associated with the first data set;

selecting a test set of inputs for the neural network;

obtaining a corresponding set of outputs, from the neural network, by applying the neural network to the test set of inputs, the neural network outputs comprising a second data set, the second data set comprising synthetic data;

estimating one or more corresponding functions that model the test set of inputs to the corresponding set of neural network outputs;

indexing the estimated one or more functions to the first data set; and

storing the indexed functions and datasets in a function index.

16. The system of claim 15 , wherein the operations further comprise:

receiving a search query indicating at least one search query function;

searching the function index based on the search query; and

returning, based on the search, the first data set.

17. The system of claim 16 , wherein searching the function index based on the search query comprises matching the search query function to a function stored in the function index.

18. The system of claim 17 , wherein matching the search query comprises finding a fuzzy match between the search query function and a function stored in the function index.

19. The system of claim 15 , wherein searching the function index based on the search query comprises:

clustering one or more functions of the function index to form a plurality of function clusters; and

matching the search query function to a first cluster of the plurality of clusters.

20. The system of claim 19 , wherein matching the search query function to a first cluster comprises:

generating a score for the first cluster indicating a degree of belongingness of the search query function to the first cluster; and

determining that the score exceeds a predetermined threshold.