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Found 5 papers, 3 papers with code
Balanced Marginal and Joint Distributional Learning via Mixture Cramer-Wold Distance
This measure enables us to capture both marginal and joint distributional information simultaneously, as it incorporates a mixture measure with point masses on standard basis vectors.
Joint Distributional Learning via Cramer-Wold Distance
The assumption of conditional independence among observed variables, primarily used in the Variational Autoencoder (VAE) decoder modeling, has limitations when dealing with high-dimensional datasets or complex correlation structures among observed variables.
Causally Disentangled Generative Variational AutoEncoder
We present a new supervised learning technique for the Variational AutoEncoder (VAE) that allows it to learn a causally disentangled representation and generate causally disentangled outcomes simultaneously.
Distributional Learning of Variational AutoEncoder: Application to Synthetic Data Generation
The Gaussianity assumption has been consistently criticized as a main limitation of the Variational Autoencoder (VAE) despite its efficiency in computational modeling.
EXoN: EXplainable encoder Network
To improve the performance of our VAE in a classification task without the loss of performance as a generative model, we employ a new semi-supervised classification method called SCI (Soft-label Consistency Interpolation).
