  Measurements of neutrinos at and below 10 GeV provide unique constraints of
neutrino oscillation parameters as well as probes of potential Non-Standard
Interactions (NSI). The IceCube Neutrino Observatory's DeepCore array is
designed to detect neutrinos down to GeV energies. IceCube has built the
world's largest data set of neutrinos >10 GeV, making searches for NSI a
computational challenge. This work describes the use of convolutional neural
networks (CNNs) to improve the energy reconstruction resolution and speed of
reconstructing O(10 GeV) neutrino events in IceCube. Compared to current
likelihood-based methods which take seconds to minutes, the CNN is expected to
provide approximately a factor of 2 improvement in energy resolution while
reducing the reconstruction time per event to milliseconds, which is essential
for processing large datasets.
