  Normalizing flows have grown more popular over the last few years; however,
they continue to be computationally expensive, making them difficult to be
accepted into the broader machine learning community. In this paper, we
introduce a simple one-dimensional one-layer network that has closed form
Lipschitz constants; using this, we introduce a new Exact-Lipschitz Flow (ELF)
that combines the ease of sampling from residual flows with the strong
performance of autoregressive flows. Further, we show that ELF is provably a
universal density approximator, more computationally and parameter efficient
compared to a multitude of other flows, and achieves state-of-the-art
performance on multiple large-scale datasets.
