Autonomic horizontal exploration in neural networks transfer learning
US11455540B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 15, 2019 |
| Grant date | Sep 27, 2022 |
| Priority date | — |
| Expiry date | May 7, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N5/02
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
An autonomic function is caused to execute in an artificial intelligence environment to detect a new problem space. Using the autonomic function, a first model is selected. The first model includes a first trained neural network corresponding to a first ontology. A second model is automatically identified. the second model includes a second trained neural network corresponding to a second ontology. A layer is autonomically extracted from the second model and inserted into a location in the first model. A vector transformation is automatically constructed to transform an output vector of a previous layer in an immediately previous location in the model relative to the location. The layer is automatically fused in the first model using the transformed output vector as input to the layer, the fusing forming a fused model that is operable on an ontology of the new problem space.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.