Encodings for reversible sparse dimensionality reduction
US10970629B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 24, 2017 |
| Grant date | Apr 6, 2021 |
| Priority date | — |
| Expiry date | Jun 11, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/20
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present disclosure is directed to reducing model size of a machine learning model with encoding. The input to a machine learning model may be encoded using a probabilistic data structure with a plurality of mapping functions into a lower dimensional space. Encoding the input to the machine learning model results in a compact machine learning model with a reduced model size. The compact machine learning model can output an encoded representation of a higher-dimensional space. Use of such a machine learning model can include decoding the output of the machine learning model into the higher dimensional space of the non-encoded input.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.