detailed critique, and we appreciate your help in presenting our work as best as possible

We want to thank the reviewers greatly for the time and effort put into these reviews. R4), and that the "conceptual difference to previous research is a big strength." "wider" approach to building normalizing flow-based models is more than just a way to improve performance, noting Our work uncovers challenges that are unique to boosting on normalizing flows. Only analytically invertible flows can be boosted for variational inference (Section 5.1, and Figure 2) In regards to R1 and R3's critique on further differentiating our work with boosted density estimation [Rosset-Segal, '02] and generative models [Grover-Ermon, '18]: We show that the change-of-variables formula can be recursively We felt that proofs of boosting's expressiveness to be outside R2 writes "there are two bottlenecks in NF expressivity--the base distribution We appreciate reviewers taking the time to check for correctness. We stand by Eq. (10), (c 1) (c 1)