HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals (bibtex)
by Chao Ma, Sebastian Tschiatschek, Yingzhen Li, Richard Turner, José Miguel Hernández-Lobato, Cheng Zhang
Abstract:
In this paper, we propose a very simple but effective VAE model (HM-VAE) that can handle real-valued data with heterogeneous marginals, meaning that they have drastically distinct marginal distributions, statistical properties as well as semantics. Preliminary resultsshow that the HM-VAE can learn distributions with heterogeneous marginal distributions,whereas the vanilla VAEs fails.
Reference:
HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous MarginalsChao Ma, Sebastian Tschiatschek, Yingzhen Li, Richard Turner, José Miguel Hernández-Lobato, Cheng Zhang In Proceedings of The 2nd Symposium on Advances in Approximate Bayesian Inference PMLR volume 118 2020.
Bibtex Entry:
@InProceedings{pmlr-v118-ma20a,
  title     = 	 { HM-VAEs: a Deep Generative Model for
 Real-valued Data with Heterogeneous Marginals},
  author    = 	 {Ma, Chao and Tschiatschek, Sebastian and Li, Yingzhen and Turner, Richard and José Miguel Hernández-Lobato and Zhang, Cheng},
  booktitle = 	 {Proceedings of The 2nd Symposium on
 Advances in Approximate Bayesian Inference},
  pages     = 	 {1--8},
  year      = 	 {2020},
  volume    = 	 {118},
  series    = 	 {Proceedings of Machine Learning Research},
  publisher = 	 {PMLR},
  url       = 	 {http://proceedings.mlr.press/v118/ma20a/ma20a.pdf},
  tag       = {PMI},
  abstract  = {In this paper, we propose a very simple but effective VAE model (HM-VAE) that can handle real-valued data with heterogeneous marginals, meaning that they have drastically distinct marginal distributions, statistical properties as well as semantics. Preliminary resultsshow that the HM-VAE can learn distributions with heterogeneous marginal distributions,whereas the vanilla VAEs fails.}
}
Powered by bibtexbrowser