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Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayes
We extend the method of Bayesian causal structure learning using GFlowNets to learn not only the posterior distribution over the structure, but also the parameters of a linear-Gaussian model.
Semi-supervised Learning of Galaxy Morphology using Equivariant Transformer Variational Autoencoders
We show that this novel architecture leads to improvements in accuracy when used for the galaxy morphology classification task on the Galaxy Zoo data set.
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