System and method for machine learning architecture for partially-observed multimodal data
US12033083B2 · kind B2 · utility
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Key dates
| Filing date | May 22, 2020 |
| Grant date | Jul 9, 2024 |
| Priority date | — |
| Expiry date | Sep 17, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/048
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Variational Autoencoders (VAEs) have been shown to be effective in modeling complex data distributions. Conventional VAEs operate with fully-observed data during training. However, learning a VAE model from partially-observed data is still a problem. A modified VAE framework is proposed that can learn from partially-observed data conditioned on the fully-observed mask. A model described in various embodiments is capable of learning a proper proposal distribution based on the missing data. The framework is evaluated for both high-dimensional multimodal data and low dimensional tabular data.
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