Annealed dropout training of neural networks
US10380484B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 22, 2015 |
| Grant date | Aug 13, 2019 |
| Priority date | — |
| Expiry date | Jun 13, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Systems and methods for training a neural network to optimize network performance, including sampling an applied dropout rate for one or more nodes of the network to evaluate a current generalization performance of one or more training models. An optimized annealing schedule may be generated based on the sampling, wherein the optimized annealing schedule includes an altered dropout rate configured to improve a generalization performance of the network. A number of nodes of the network may be adjusted in accordance with a dropout rate specified in the optimized annealing schedule. The steps may then be iterated until the generalization performance of the network is maximized.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.