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Bayesian Domain Invariant Learning via Posterior Generalization of Parameter Distributions
We first propose a theorem to show that the invariant posterior of parameters can be implicitly inferred by aggregating posteriors on different training domains.
Be Bayesian by Attachments to Catch More Uncertainty
Bayesian Neural Networks (BNNs) have become one of the promising approaches for uncertainty estimation due to the solid theorical foundations.
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