Multifidelity simulation-based inference enables accurate field-level weak lensing cosmology with 60-100 high-fidelity N-body simulations via pre-training on log-normal mocks.
Deep ensembles secretly perform empirical bayes
2 Pith papers cite this work. Polarity classification is still indexing.
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SHRUG-FM fuses geophysical OOD detection, embedding-space OOD detection, and predictive uncertainty via a shallow decision tree to let foundation models abstain from unreliable outputs on burn scar, flood, and landslide tasks.
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Field-level weak lensing cosmology with $<100$ simulations using multifidelity simulation-based inference
Multifidelity simulation-based inference enables accurate field-level weak lensing cosmology with 60-100 high-fidelity N-body simulations via pre-training on log-normal mocks.
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SHRUG-FM: Reliability-Aware Foundation Models for Earth Observation
SHRUG-FM fuses geophysical OOD detection, embedding-space OOD detection, and predictive uncertainty via a shallow decision tree to let foundation models abstain from unreliable outputs on burn scar, flood, and landslide tasks.