RobustNeuralNetworks.jl: a Package for Machine Learning and Data-Driven
  Control with Certified Robustness
Nicholas H. Barbara Max Revay Ruigang Wang Jing Cheng Ian R. Manchester
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.