System, method, and computer program product for reducing dataset biases in natural language inference tasks using unadversarial training
US12333396B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 10, 2023 |
| Grant date | Jun 17, 2025 |
| Priority date | — |
| Expiry date | May 10, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/0455
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Provided are systems for generating a machine learning model for classification tasks using unadversarial training that include a processor to perform an unadversarial training procedure to train a machine learning model to provide a trained machine learning model. When performing the unadversarial training procedure, the processor is programmed or configured to receive a training dataset including a plurality of training samples; generate a noise vector for the plurality of training samples based on a uniform distribution; perturb each training sample of the plurality of training samples; obtain a gradient; generate an updated noise vector based on the gradient; perturb each training sample of the plurality of training samples based on the updated noise vector; and update a model weight of the machine learning model based on the second plurality of perturbed training samples to provide the trained machine learning model. Methods and computer program products are also provided.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.