Machine learning through multiple layers of novel machine trained processing nodes
US10867247B1 · kind B1 · utility
Assignee
Inventor
Key dates
| Filing date | Aug 9, 2016 |
| Grant date | Dec 15, 2020 |
| Priority date | — |
| Expiry date | Jul 14, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/048
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Some embodiments of the invention provide efficient, expressive machine-trained networks for performing machine learning. The machine-trained (MT) networks of some embodiments use novel processing nodes with novel activation functions that allow the MT network to efficiently define with fewer processing node layers a complex mathematical expression that solves a particular problem (e.g., face recognition, speech recognition, etc.). In some embodiments, the same activation function (e.g., a cup function) is used for numerous processing nodes of the MT network, but through the machine learning, this activation function is configured differently for different processing nodes so that different nodes can emulate or implement two or more different functions (e.g., two or more Boolean logical operators, such as XOR and AND). The activation function in some embodiments is a periodic function that can be configured to implement different functions (e.g., different sinusoidal functions).
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.