Deterministic Autoencoder using Wasserstein loss for tabular data generation

Tabular data generation is a complex task due to its distinctive characteristics and inherent complexities. While Variational Autoencoders have been adapted from the computer vision domain for tabular data synthesis, their reliance on non-deterministic latent space regularization introduces limitati...

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Vydané v:	Neural networks Ročník 185; s. 107208
Hlavní autori:	Wang, Alex X., Nguyen, Binh P.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Elsevier Ltd 01.05.2025
Predmet:	Algorithms Autoencoder Deep Learning Deep neural networks Generative AI Humans Latent space interpolation Neural Networks, Computer Tabular data synthesis Wasserstein Autoencoder Deep neural networks Generative AI Tabular data synthesis Latent space interpolation Wasserstein Autoencoder
ISSN:	0893-6080, 1879-2782, 1879-2782
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