"novel and insightful " with " a very intriguing and intuitive explanation for the mechanics of pruning " and " well

We thank the reviewers for their detailed, valuable reviews. We agree with the reviewers' concern and will ensure the final version includes the following data that These studies use a "more robust pruning regime" ( ResNet20 results, seem to apply to less "modern" regimes ( R3 Generalization gap vs. test accuracy; train accuracy not reported: We will update our manuscript to clearly discuss training accuracies and plot the generalization gaps. R3 "Pearson correlation and slope do not give an accurate characterization": We will move methodological details to the "main body" R3 Hyperparameter choices ("These networks reach much lower accuracy than expected... L1/L2 regular-30 Section 2 justified our exposition's focus on less-regularized models, which is not unprecedented: It led to our exploring pruning of the last convolutional layers of VGG11/ResNet18. R3 "[DSD] is worth a comparison" and "the claim... is hard to extract": DSD, we show that the parameters can reenter at zero or their original values (Figure D.2) while achieving the full R3 "[15] is not found to improve generalization":