Graphical Generative Adversarial Networks

NeurIPS 2018 Chongxuan LiMax WellingJun ZhuBo Zhang

We propose Graphical Generative Adversarial Networks (Graphical-GAN) to model structured data. Graphical-GAN conjoins the power of Bayesian networks on compactly representing the dependency structures among random variables and that of generative adversarial networks on learning expressive dependency functions... (read more)

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