Neural adapter for classical machine learning (ML) models
US11922315B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 26, 2019 |
| Grant date | Mar 5, 2024 |
| Priority date | — |
| Expiry date | Oct 17, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T1/20
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Solutions for adapting machine learning (ML) models to neural networks (NNs) include receiving an ML pipeline comprising a plurality of operators; determining operator dependencies within the ML pipeline; determining recognized operators; for each of at least two recognized operators, selecting a corresponding NN module from a translation dictionary; and wiring the selected NN modules in accordance with the operator dependencies to generate a translated NN. Some examples determine a starting operator for translation, which is the earliest recognized operator having parameters. Some examples connect inputs of the translated NN to upstream operators of the ML pipeline that had not been translated. Some examples further tune the translated NN using backpropagation. Some examples determine whether an operator is trainable or non-trainable and flag related parameters accordingly for later training. Some examples determine whether an operator has multiple corresponding NN modules within the translation dictionary and make an optimized selection.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.