  We present a deep learning approach for vertex reconstruction of
neutrino-nucleus interaction events, a problem in the domain of high energy
physics. In this approach, we combine both energy and timing data that are
collected in the MINERvA detector to perform classification and regression
tasks. We show that the resulting network achieves higher accuracy than
previous results while requiring a smaller model size and less training time.
In particular, the proposed model outperforms the state-of-the-art by 4.00% on
classification accuracy. For the regression task, our model achieves 0.9919 on
the coefficient of determination, higher than the previous work (0.96).
