6 citations · 7 across the 5 of their papers we have counts for
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stat.ML2023★ 1 cited
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…
stat.ML2023★ 6 cited
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…
stat.ML2021
EXoN: EXplainable encoder Network
SeungHwan An, Hosik Choi, Jong-June Jeon
We propose a new semi-supervised learning method of Variational AutoEncoder (VAE) which yields a customized and explainable latent space by EXplainable encoder Network (EXoN). Cust…