Efficient determination of optimized learning settings of neural networks
US11093826B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 5, 2016 |
| Grant date | Aug 17, 2021 |
| Priority date | — |
| Expiry date | Dec 20, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N7/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Optimized learning settings of neural networks are efficiently determined by an apparatus including a processor and one or more computer readable mediums collectively including instructions that, when executed by the processor, cause the processor to train a first neural network with a learning setting; extract tentative weight data from the first neural network with the learning setting; calculate an evaluation value of the first neural network with the learning setting; and generate a predictive model for predicting an evaluation value of a second neural network with a new setting based on tentative weight data of the second neural network by using a relationship between the tentative weight data of the first neural network and the evaluation value of the first neural network.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.