  Neural networks are typically sensitive to small input perturbations, leading
to unexpected or brittle behaviour. We present RobustNeuralNetworks.jl: a Julia
package for neural network models that are constructed to naturally satisfy a
set of user-defined robustness constraints. The package is based on the
recently proposed Recurrent Equilibrium Network (REN) and Lipschitz-Bounded
Deep Network (LBDN) model classes, and is designed to interface directly with
Julia's most widely-used machine learning package, Flux.jl. We discuss the
theory behind our model parameterization, give an overview of the package, and
provide a tutorial demonstrating its use in image classification, reinforcement
learning, and nonlinear state-observer design.
