Apparatus and method for training neural networks using small, heterogeneous cohorts of training data
US11373298B2 · kind B2 · utility
Assignee
Inventor
Key dates
| Filing date | Mar 28, 2019 |
| Grant date | Jun 28, 2022 |
| Priority date | — |
| Expiry date | Apr 9, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20092
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A system including processing circuitry configured to train a model for predicting from input data at least one predicted output, wherein the processing circuitry is configured to: receive a plurality of training data sets; receive from a user a selection of a first characteristic including positive and negative samples which are relevant variations significant to prediction of the at least one predicted output; receive from the user a selection of a second characteristic including an irrelevant sample which is a spurious variation irrelevant to the prediction of the predicted output; perform positive supervision of the model using the first characteristic such that the training of the model is sensitive to the positive and negative samples of the first characteristic; and perform negative supervision of the model using the second characteristic such that the training of the model is insensitive to the irrelevant sample of the second characteristic.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.