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Missing Features Reconstruction Using a Wasserstein Generative Adversarial Imputation Network
Missing data is one of the most common preprocessing problems. In this paper, we experimentally research the use of generative and non-generative models for feature reconstruction. Variational Autoencoder with Arbitrary Conditioning (VAEAC) and Generative Adversarial Imputation Network (GAIN) were r...
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| Publicado no: | Computational Science – ICCS 2020 |
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| Main Authors: | , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2020
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7303681/ https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1007/978-3-030-50423-6_17 |
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