Learning embedded representation of a correlation matrix to a network with machine learning
US12299395B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 29, 2022 |
| Grant date | May 13, 2025 |
| Priority date | — |
| Expiry date | Jan 3, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/0985
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
System, method, and a computer program product for generating embeddings are provided. A machine learning framework generates a fully connected network from a dataset associated with words. The words correspond to nodes in the fully connected network. The weights are associated with correlations between the nodes and correspond to the links in the fully connected network. The machine learning framework transforms the correlations corresponding to the links into distances. The machine learning framework generates a sparse network from the fully connected network based on the distances. From the sparse network, machine learning framework determines sentence structures by traversing the nodes. Using the sentence structures, the machine learning framework uses a neural network to generate embeddings in the embedded space.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.