Distributed learning of composite machine learning models
US11481627B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 30, 2019 |
| Grant date | Oct 25, 2022 |
| Priority date | — |
| Expiry date | Apr 28, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/126
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Computer-implemented techniques for learning composite machine learned models are disclosed. Benefits to implementors of the disclosed techniques include allowing non-machine learning experts to use the techniques for learning a composite machine learned model based on a learning dataset, reducing or eliminating the explorative trial and error process of manually tuning architectural parameters and hyperparameters, and reducing the computing resource requirements and model learning time for learning composite machine learned models. The techniques improve the operation of distributed learning computing systems by reducing or eliminating straggler effects and by reducing or minimizing synchronization latency when executing a composite model search algorithm for learning a composite machine learned model.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.