Mutual information neural estimation with Eta-trick
US11630989B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 9, 2020 |
| Grant date | Apr 18, 2023 |
| Priority date | — |
| Expiry date | Jan 14, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/088
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A computing device receives a data X and Y, each having N samples. A function f(x,y) is defined to be a trainable neural network based on the data X and the data Y. A permuted version of the data Y is created. A loss mean is computed based on the trainable neural network f(x,y), the permuted version of the sample data Y, and a trainable scalar variable η. A loss with respect to the scalar variable η and the trainable neural network is minimized. Upon determining that the loss is at or below the predetermined threshold, estimating a mutual information (MI) between a test data XT and YT. If the estimated MI is above a predetermined threshold, the test data XT and YT is deemed to be dependent. Otherwise, it is deemed to be independent.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.