Fixed, random, recurrent matrices for increased dimensionality in neural networks
US12182719B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 28, 2020 |
| Grant date | Dec 31, 2024 |
| Priority date | — |
| Expiry date | Jun 5, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/09
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method of operating a neural network. The input layer of the network may have n input nodes connected to output nodes via a hidden layer. The hidden layer may include m hidden nodes. The n input nodes may connect to a subset of k nodes of the m hidden nodes via respective synaptic connections, to which training weights are associated, which form an n×k input matrix Win, whereas a subset of m−k nodes of the hidden layer are not connected by any node of the input layer. Running the network may include performing a first matrix vector multiplication between the input matrix Win and a vector of values obtained in output of the input nodes and a second matrix vector multiplication between a fixed matrix Wrec of fixed weights and a vector of values obtained in output of the m nodes of the hidden layer.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.