Neural networks for scalable continual learning in domains with sequentially learned tasks
US12020164B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 18, 2019 |
| Grant date | Jun 25, 2024 |
| Priority date | — |
| Expiry date | Sep 9, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/006
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for scalable continual learning using neural networks. One of the methods includes receiving new training data for a new machine learning task; training an active subnetwork on the new training data to determine trained values of the active network parameters from initial values of the active network parameters while holding current values of the knowledge parameters fixed; and training a knowledge subnetwork on the new training data to determine updated values of the knowledge parameters from the current values of the knowledge parameters by training the knowledge subnetwork to generate knowledge outputs for the new training inputs that match active outputs generated by the trained active subnetwork for the new training inputs.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.