Speaker
Daniel Douglas
(SLAC National Accelerator Laboratory)
Description
With the rising importance of large sequential models in neutrino imaging and reconstruction tasks, a robust estimation of uncertainty at each stage of reconstruction is essential. These chained models produce valuable physical representations of the evolution of a neutrino interaction, which are of interest to many disparate fields of study. This talk will discuss methods available for estimating aleatoric and epistemic uncertainties via propagation of input uncertainties and ab initio, learning from input distributions.
| Type of contribution | Talk: 15 minutes. |
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Primary author
Daniel Douglas
(SLAC National Accelerator Laboratory)
