Automated neural network generation using fitness estimation
US10685286B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 30, 2019 |
| Grant date | Jun 16, 2020 |
| Priority date | — |
| Expiry date | Jul 30, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/0985
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method of generating a neural network includes iteratively performing operations including generating, for each neural network of a population, a matrix representation. The matrix representation of a particular neural network includes rows of values, where each row corresponds to a set of layers of the particular neural network and each value specifies a hyperparameter of the set of layers. The operations also include providing the matrix representations as input to a relative fitness estimator that is trained to generate estimated fitness data for neural networks of the population. The estimated fitness data are based on expected fitness of neural networks predicted by the relative fitness estimator. The operations further include generating, based on the estimated fitness data, a subsequent population of neural networks. The method also includes, when a termination condition is satisfied, outputting data identifying a neural network as a candidate neural network.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.