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Found 2 papers, 1 papers with code
Demystifying amortized causal discovery with transformers
Consistent with classical approaches, good performance is achieved when we have a good prior on the test data, and the underlying model is identifiable.
PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modelling
Recent advances have shown that GP priors, or their finite realisations, can be encoded using deep generative models such as variational autoencoders (VAEs).
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