Tabular data generation for machine learning model training system
US11436438B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 22, 2021 |
| Grant date | Sep 6, 2022 |
| Priority date | — |
| Expiry date | Dec 22, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/094
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
(A) Conditional vectors are defined. (B) Latent observation vectors are generated using a predefined noise distribution function. (C) A forward propagation of a generator model is executed with the conditional vectors and the latent observation vectors as input to generate an output vector. (D) A forward propagation of a decoder model of a trained autoencoder model is executed with the generated output vector as input to generate a plurality of decoded vectors. (E) Transformed observation vectors are selected from transformed data based on the defined plurality of conditional vectors. (F) A forward propagation of a discriminator model is executed with the transformed observation vectors, the conditional vectors, and the decoded vectors as input to predict whether each transformed observation vector and each decoded vector is real or fake. (G) The discriminator and generator models are updated and (A) through (G) are repeated until training is complete.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.