System and method for machine learning architecture with variational hyper-RNN
US11615305B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 22, 2020 |
| Grant date | Mar 28, 2023 |
| Priority date | — |
| Expiry date | Dec 19, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/0475
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A variational hyper recurrent neural network (VHRNN) can be trained by, for each step in sequential training data: determining a prior probability distribution for a latent variable from a prior network of the VHRNN using an initial hidden state; determining a hidden state from a recurrent neural network (RNN) of the VHRNN using an observation state, the latent variable and the initial hidden state; determining an approximate posterior probability distribution for the latent variable from an encoder network of the VHRNN using the observation state and the initial hidden state; determining a generating probability distribution for the observation state from a decoder network of the VHRNN using the latent variable and the initial hidden state; and maximizing a variational lower bound of a marginal log-likelihood of the training data. The trained VHRNN can be used to generate sequential data.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.