  Normalizing Flows are generative models that directly maximize the
likelihood. Previously, the design of normalizing flows was largely constrained
by the need for analytical invertibility. We overcome this constraint by a
training procedure that uses an efficient estimator for the gradient of the
change of variables formula. This enables any dimension-preserving neural
network to serve as a generative model through maximum likelihood training. Our
approach allows placing the emphasis on tailoring inductive biases precisely to
the task at hand. Specifically, we achieve excellent results in molecule
generation benchmarks utilizing $E(n)$-equivariant networks. Moreover, our
method is competitive in an inverse problem benchmark, while employing
off-the-shelf ResNet architectures.
