  IceCube DeepCore is an extension of the IceCube Neutrino Observatory designed
to measure GeV scale atmospheric neutrino interactions for the purpose of
neutrino oscillation studies. Distinguishing muon neutrinos from other flavors
and reconstructing inelasticity are especially difficult tasks at GeV scale
energies in IceCube DeepCore due to sparse instrumentation. Convolutional
neural networks (CNNs) have been found to have better success at neutrino event
reconstruction than conventional likelihood-based methods. In this
contribution, we present a new CNN model that exploits time and depth
translational symmetry in IceCube DeepCore data and present the model's
performance, specifically for flavor identification and inelasticity
reconstruction.
