Flexible parameter sharing for multi-task learning
US11915120B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 17, 2020 |
| Grant date | Feb 27, 2024 |
| Priority date | — |
| Expiry date | Nov 23, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Systems and methods for flexible parameter sharing for multi-task learning are provided. A training method can include obtaining a test input, selecting a particular task from one or more tasks, and training a multi-task machine-learned model for the particular task by performing a forward pass using the test input and one or more connection probability matrices to generate a sample distribution of test outputs, training the components of the machine-learned model based at least in part on the sample distribution, and performing a backwards pass to train a connection probability matrix of the multi-task machine-learned model using a straight-through Gumbel-softmax approximation.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.