We thank the reviewers for their appreciative and thoughtful feedback. Reviewer 1. "However, the authors fail to bring the result to their impact of the current state OT, or any novel stochastic optimization algorithm designed to compute it faster. We will further emphasize these aspects. " A measure with 0 mean. Reviewer 2. "If the paper could show the formula for that case [TV] that would be Figure 1: Large dimensions need more samples to approximate the moments of the unbalanced optimal transport plan. Reviewer 3. ""Figure 1 illustrates the convergence"... the convergence of what?" "Figure 2 is also difficult to understand.

We thank the reviewers for the thoughtful feedback in these difficult times caused by the global COVID-19 pandemic. QM9 is used for training, the model must be based on LCAO, and QDF achieved high extrapolation performance. We emphasize that even this LDA-like HK map achieved high extrapolation performance. We will address this in future work. Of course, QDF can be proposed without a comparison to GCN.

We thank reviewers for their thoughtful feedback. We outperform all the reviewers' additional references that "One model to learn them all" is We will add all mentioned references but we stress that they do not hurt our novelty claim nor our results. MIL-NCE by 2% in both UCF and HMDB, while enabling a task (ESC-50) that would not be possible with MIL-NCE. We agree that our novelty does not lie in the loss which is indeed not novel. We will clarify the impact of the deflation contribution in the paper.

In the first, we used the latest version of Rosetta (3.10) to design

We thank R1 for their comments and will emphasize the broader implications of our work on model explainability. R2 asked to contrast using (i) influence functions to measure the importance of training points with (ii) existing These papers address a different problem setting from ours and their methods are correspondingly distinct. Despite their differences, these methods could be complementary, as R2 suggested. We will include this discussion and we thank R2 for pointing it out. R3 asked if our empirical findings hold for non-convex models.