Uncertainty analysis for neural networks
US12360277B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 9, 2021 |
| Grant date | Jul 15, 2025 |
| Priority date | — |
| Expiry date | Feb 28, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/12
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method includes receiving geophysical data representative of a geophysical structure; providing the geophysical data as one or more input data to a neural network; training the neural network to reconstruct the geophysical structure that was received and provide one or more uncertainty metrics for one or more features of the geophysical structure that is reconstructed; reconstructing, using the neural network that has been trained, the geophysical structure; and determining, using the neural network that has been trained, the one or more uncertainty metrics by implementing a second drop out condition on the one or more nodes of the one or more hidden layers of the neural network. The training is performed at least partially by implementing a first drop out condition on one or more nodes of one or more hidden layers of the neural network to randomly set an output of the one or more nodes to zero.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.