9 citations · 16 across the 9 of their papers we have counts for
4 papers · 1 filter
Balanced Marginal and Joint Distributional Learning via Mixture Cramer-Wold Distance
Seunghwan An, Sungchul Hong, Jong-June Jeon
In the process of training a generative model, it becomes essential to measure the discrepancy between two high-dimensional probability distributions: the generative distribution a…
Joint Distributional Learning via Cramer-Wold Distance
Seunghwan An, Jong-June Jeon
The assumption of conditional independence among observed variables, primarily used in the Variational Autoencoder (VAE) decoder modeling, has limitations when dealing with high-di…
Causally Disentangled Generative Variational AutoEncoder
Seunghwan An, Kyungwoo Song, Jong-June Jeon
We present a new supervised learning technique for the Variational AutoEncoder (VAE) that allows it to learn a causally disentangled representation and generate causally disentangl…
Distributional Learning of Variational AutoEncoder: Application to Synthetic Data Generation
Seunghwan An, Jong-June Jeon
The Gaussianity assumption has been consistently criticized as a main limitation of the Variational Autoencoder (VAE) despite its efficiency in computational modeling. In this pape…