Review for NeurIPS paper: Woodbury Transformations for Deep Generative Flows

The paper proposes to parameterize a linear transformation as a low-rank update to an identity matrix, and then use the Woodbury matrix identity to efficiently compute its inverse and the Sylvester determinant identity to efficiently compute its determinant. Some reviewers expressed concerns regarding novelty, which I share. Indeed, the proposed linear flows are a fairly straightforward application of well-known matrix-algebra techniques for inverting and calculating the determinant of low-rank updates. For that reason, I doubt this paper contains much new information for normalizing-flow experts, although it may be useful to a broader machine-learning audience. Having said that, this paper is well-written and well-executed, contains some novel extensions to the basic idea, and it's likely the first published version of Woodbury flows with careful experimental comparisons to alternatives.