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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2 Pith papers cite this work. Polarity classification is still indexing.
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A single-layer GRU model using NASA Black Marble nightlights nowcasts Italian municipal taxable income with 1.07 million euro median error (4% of median), statistically outperforming persistence, fixed effects, ARDL, and spatial econometric benchmarks out-of-sample on 2020-2021.
citing papers explorer
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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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Nowcasting Italian Municipal Income with Nightlights: A Deep Learning Approach
A single-layer GRU model using NASA Black Marble nightlights nowcasts Italian municipal taxable income with 1.07 million euro median error (4% of median), statistically outperforming persistence, fixed effects, ARDL, and spatial econometric benchmarks out-of-sample on 2020-2021.