Adversarial training for event sequence analysis
US11507878B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 10, 2019 |
| Grant date | Nov 22, 2022 |
| Priority date | — |
| Expiry date | Jun 23, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/094
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques are disclosed for the generation of adversarial training data through sequence perturbation, for a deep learning network to perform event sequence analysis. A methodology implementing the techniques according to an embodiment includes applying a long short-term memory attention model to an input data sequence to generate discriminative sequence periods and attention weights associated with the discriminative sequence periods. The attention weights are generated to indicate the relative importance of data in those discriminative sequence periods. The method further includes generating perturbed data sequences based on the discriminative sequence periods and the attention weights. The generation of the perturbed data sequences employs selective filtering or conservative adversarial training, to preserve perceptual similarity between the input data sequence and the perturbed data sequences. The input data sequence may be created by vectorizing a temporal input data stream comprising words, symbols, and the like, into a multidimensional vectorized numerical data sequence format.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.