FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.
and Lewis, Geraint F
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A differentiable forward model and likelihood enable fully probabilistic, high-dimensional inference over continuum morphologies of the Galactic Center gamma-ray Excess.
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Local Conformal Predictions for Calibrated Surrogates
FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.
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High-dimensional inference for the $\gamma$-ray sky with differentiable programming
A differentiable forward model and likelihood enable fully probabilistic, high-dimensional inference over continuum morphologies of the Galactic Center gamma-ray Excess.