RobustNeuralNetworks.jl: a Package for Machine Learning and Data-Driven Control with Certified Robustness
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 15 of its 30 references that are in the map. First posted on arXiv 22 Jun 2023.
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15 of 30 references.
References · 15
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- 2021Youla-REN: Learning Nonlinear Feedback Policies with Robust Stability Guaranteescited by 10
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3 more
- 2022Synthesis of Stabilizing Recurrent Equilibrium Network Controllerscited by 8
- 2023Unconstrained Parametrization of Dissipative and Contracting Neural Ordinary Differential Equationscited by 8
- 2023Learning Over Contracting and Lipschitz Closed-Loops for Partially-Observed Nonlinear Systems (Extended Version)cited by 6
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Citing papers per year: 2025: 1.
1 paper.