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RobustNeuralNetworks.jl: a Package for Machine Learning and Data-Driven Control with Certified Robustness
JuliaCon Proceedings (JuliaCon), 2023
- OOD
Main:10 Pages
11 Figures
Bibliography:2 Pages
1 Tables
Abstract
Neural networks are typically sensitive to small input perturbations, leading to unexpected or brittle behaviour. We presentthis http URL: 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,this http URL. 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.
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