Method and system for predicting discrete sequences using deep context tree weighting
US11763170B2 · kind B2 · utility
Assignees
Inventors
Key dates
| Filing date | Feb 5, 2018 |
| Grant date | Sep 19, 2023 |
| Priority date | — |
| Expiry date | Oct 3, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Systems and methods use deep, convolutional neural networks over exponentially long history windows to learn alphabets for context tree weighting (CTW) for prediction. Known issues of depth and breadth in conventional context tree weighting predictions are addressed by the systems and methods. To deal with depth, the history can be broken into time windows, permitting the ability to look exponentially far back while having less information the further one looks back. To deal with breadth, a deep neural network classifier can be used to learn to map arbitrary length histories to a small output symbol alphabet. The sequence of symbols produced by such a classifier over the history windows would then become the input sequence to CTW.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.