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SIReN-VAE: Leveraging Flows and Amortized Inference for Bayesian Networks
Subsequent work has explored incorporating more complex distributions and dependency structures: including normalizing flows in the encoder network allows latent variables to entangle non-linearly, creating a richer class of distributions for the approximate posterior, and stacking layers of latent variables allows more complex priors to be specified for the generative model.
Graphical Residual Flows
Furthermore, our model provides performance competitive with other graphical flows for both density estimation and inference tasks.
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