Methods and systems using improved training and learning for deep neural networks
US11537851B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 7, 2017 |
| Grant date | Dec 27, 2022 |
| Priority date | — |
| Expiry date | Dec 13, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T1/20
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods and systems are disclosed using improved training and learning for deep neural networks. In one example, a deep neural network includes a plurality of layers, and each layer has a plurality of nodes. The nodes of each L layer in the plurality of layers are randomly connected to nodes of an L+1 layer. The nodes of each L+1 layer are connected to nodes in a subsequent L layer in a one-to-one manner. Parameters related to the nodes of each L layer are fixed. Parameters related to the nodes of each L+1 layers are updated. In another example, inputs for the input layer and labels for the output layer of a deep neural network are determined related to a first sample. A similarity between different pairs of inputs and labels is estimated using a Gaussian regression process.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.