Minimum-example/maximum-batch entropy-based clustering with neural networks
US11475236B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 21, 2020 |
| Grant date | Oct 18, 2022 |
| Priority date | — |
| Expiry date | Jan 13, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/045
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A computing system can include an embedding model and a clustering model. The computing system input each of the plurality of inputs into the embedding model and receiving respective embeddings for the plurality of inputs as outputs of the embedding model. The computing system can input the respective embeddings for the plurality of inputs into the clustering model and receiving respective cluster assignments for the plurality of inputs as outputs of the clustering model. The computing system can evaluate a clustering loss function that evaluates a first average, across the plurality of inputs, of a respective first entropy of each respective probability distribution; and a second entropy of a second average of the probability distributions for the plurality of inputs. The computing system can modify parameter(s) of one or both of the clustering model and the embedding model based on the clustering loss function.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.