We believe that our DNN method is accurate for two reasons. Firstly, a DNN that is deep enough will not overfit if it's in the "modern" interpolating regime . Secondly, each DNN model, seeing limited data, captures complexities and nuances in the data, which results in individual predictions with high variance; however, the overall model generalizes well when predictions are averaged together via ensembling . Importantly, training the 16 DNNs in our study and evaluating predictions for millions of samples is still computationally tractable from a cost standpoint.