RobustNeuralNetworks.jl: a Package for Machine Learning and Data-Driven Control with Certified Robustness
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915523166470144 |
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| author | Barbara, Nicholas H. Revay, Max Wang, Ruigang Cheng, Jing Manchester, Ian R. |
| author_facet | Barbara, Nicholas H. Revay, Max Wang, Ruigang Cheng, Jing Manchester, Ian R. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_12612 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | RobustNeuralNetworks.jl: a Package for Machine Learning and Data-Driven Control with Certified Robustness Barbara, Nicholas H. Revay, Max Wang, Ruigang Cheng, Jing Manchester, Ian R. Machine Learning Systems and Control 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. |
| title | RobustNeuralNetworks.jl: a Package for Machine Learning and Data-Driven Control with Certified Robustness |
| topic | Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2306.12612 |