Training artificial neural networks with reduced computational complexity
US11620514B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 28, 2018 |
| Grant date | Apr 4, 2023 |
| Priority date | — |
| Expiry date | Dec 14, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L15/16
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
At least some embodiments of the present disclosure relate to a method of training an artificial neural network (ANN) for an artificial intelligence recognition. The method includes producing, by an ANN, outputs by feeding inputs of a training data set to the ANN; determining errors of the generated outputs from target outputs of the training data set; generating a first-order derivative matrix including first-order derivatives of the errors and a second-order derivative matrix including second-order derivatives of the errors; obtaining an approximation of the first-order derivative matrix or an approximation of the second-order derivative matrix by compressing the first-order derivative matrix or the second-order derivative matrix; and updating weights of the ANN based on the approximation of the first-order derivative matrix or the approximation of the second-order derivative matrix.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.