Systems and methods for quickly searching datasets by indexing synthetic data generating models

A method for searching datasets, the method comprising:

• receiving a test dataset from a client device;
• generating, using a data model, test activation function values based on a test dataset, the test activation function values corresponding to nodes of the data model and determining whether the corresponding nodes fire;
• processing the test activation function values, the processing comprising implementing at least one of an encoding method,
• a factorizing method, or a vectorizing method on the distribution of test activation function values;
• retrieving reference activation function values from a dataset index;
• generating a similarity metric based on the reference activation function values and the processed test activation function values;
• classifying the test dataset based on the similarity metric;
• updating the dataset index based on the test data-model output;
• and
• transmitting, to the client device, information comprising the classification of the test dataset.

A system for searching datasets, comprising:

• one or more memory units storing instructions;
• and
• one or more processors that execute the instructions to perform operations comprising:
• receiving a plurality of input datasets;
• generating a plurality of candidate data-models corresponding to the input datasets;
• training the candidate data-models to generate synthetic data based on the corresponding input datasets, the training comprising setting respective architectural features of the candidate data-models;
• selecting a trained data-model based on the respective architectural features;
• training the selected data-model to generate synthetic data based on the input datasets;
• generating, using a data model, test activation function values based on a test dataset, the test activation function values corresponding to nodes of the data model and determining whether the corresponding nodes fire;
• processing the reference activation function values, the processing comprising implementing at least one of an encoding method, a factorizing method, or a vectorizing method on the reference activation function values;
• storing processed reference activation function values output in a dataset index;
• receiving a test dataset from a client device;
• generating test activation function values output using the selected data-model, based on the test dataset;
• processing the test activation function values, the processing comprising at least one of implementing an encoding method, a factorizing method, or a vectorizing method on the test activation function values;
• retrieving the processed reference activation function values from the dataset index;
• generating a similarity metric based on the processed reference activation function values and the processed test activation function values;
• classifying the test dataset based on the similarity metric;
• updating the dataset index based on the processed activation function values;
• and
• transmitting, to the client device, information comprising the classification of the test dataset.