GMRF MCVAE embeds Gaussian Markov Random Fields into VAE prior and posterior distributions to explicitly model cross-component relationships, reporting SOTA results on a synthetic Copula dataset and improved coherence on BIKED.
Let M be a complex matrix, and let E and F be two vector spaces equipped with norms ∥ · ∥ E and ∥ · ∥ F , respectively, such that for all x ∈ E, Mx ∈ F
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Multi-Component VAE with Gaussian Markov Random Field
GMRF MCVAE embeds Gaussian Markov Random Fields into VAE prior and posterior distributions to explicitly model cross-component relationships, reporting SOTA results on a synthetic Copula dataset and improved coherence on BIKED.