Deep learning using alternating direction method of multipliers
US10579922B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 8, 2014 |
| Grant date | Mar 3, 2020 |
| Priority date | — |
| Expiry date | Dec 18, 2035 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/098
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The use of the alternating direction method of multipliers (ADMM) algorithm to train a classifier may reduce the amount of classifier training time with little degradation in classifier accuracy. The training involves partitioning the training data for training the classifier into multiple data blocks. The partitions may preserve the joint distribution of input features and an output class of the training data. The training may further include performing an ADMM iteration on the multiple data blocks in an initial order using multiple worker nodes. Subsequently, the training of the classifier is determined to be completed if a stop criterion is satisfied following the ADMM iteration. Otherwise, if the stop criterion is determined to be unsatisfied following the ADMM iteration, one or more additional ADMM iterations may be performed on different orders of the multiple data blocks until the stop criterion is satisfied.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.