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Improving Out-of-Distribution Detection via Epistemic Uncertainty Adversarial Training
The quantification of uncertainty is important for the adoption of machine learning, especially to reject out-of-distribution (OOD) data back to human experts for review.
Maximum entropy kinetic matching conditions for heavy-ion collisions
Coupling hadronic kinetic theory models to fluid dynamics in phenomenological studies of heavy ion collisions requires a prescription for ``particlization''.
High Energy Physics - Phenomenology Nuclear Theory
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