# RobustNeuralNetworks.jl: a Package for Machine Learning and Data-Driven Control with Certified Robustness

Source: [RobustNeuralNetworks.jl: a Package for Machine Learning and Data-Driven Control with Certified Robustness](<https://www.sota2.com/research/paper/68fa7baf1e209131cea89e17>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T05:46:25.721Z

Authors: Nicholas H. Barbara, Max Revay, Ruigang Wang, Jing Cheng, Ian R. Manchester

Published: 2023-06-22T00:23:37.000Z

## Abstract

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 metrics. 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2306.12612>)

[PDF](<https://arxiv.org/pdf/2306.12612.pdf>)