  Normalizing flows attempt to model an arbitrary probability distribution
through a set of invertible mappings. These transformations are required to
achieve a tractable Jacobian determinant that can be used in high-dimensional
scenarios. The first normalizing flow designs used coupling layer mappings
built upon affine transformations. The significant advantage of such models is
their easy-to-compute inverse. Nevertheless, making use of affine
transformations may limit the expressiveness of such models. Recently,
invertible piecewise polynomial functions as a replacement for affine
transformations have attracted attention. However, these methods require
solving a polynomial equation to calculate their inverse. In this paper, we
explore using linear rational splines as a replacement for affine
transformations used in coupling layers. Besides having a straightforward
inverse, inference and generation have similar cost and architecture in this
method. Moreover, simulation results demonstrate the competitiveness of this
approach's performance compared to existing methods.
