FEM is a conditional energy model for hybrid Bayesian networks that uses learned embeddings and valley regularization to enable accurate multimodal posterior inference and compositional sampling.
arXiv preprint arXiv:1903.00954 , year =
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The deep SPAR model shows concurrent floods and droughts becoming more likely in the Upper Danube by 2100 under high emissions, with changes in the dependence between catchments contributing substantially to the increase.
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Free Energy Manifold: Score-Based Inference for Hybrid Bayesian Networks
FEM is a conditional energy model for hybrid Bayesian networks that uses learned embeddings and valley regularization to enable accurate multimodal posterior inference and compositional sampling.
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Exploring climate change effects on concurrent floods and concurrent droughts via statistical deep learning
The deep SPAR model shows concurrent floods and droughts becoming more likely in the Upper Danube by 2100 under high emissions, with changes in the dependence between catchments contributing substantially to the increase.