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Wei-TianHao committed Aug 16, 2024
2 parents 4eddef0 + 88a7abe commit c9fc903
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5 changes: 5 additions & 0 deletions .github/workflows/CI.yml
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env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }}
- name: Upload coverage reports to Codecov
uses: codecov/[email protected]
env:
token: ${{ secrets.CODECOV_TOKEN }}

5 changes: 3 additions & 2 deletions README.md
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# ModelVerification.jl

![CI](https://github.com/intelligent-control-lab/ModelVerification.jl/actions/workflows/CI.yml/badge.svg)
[![CI](https://github.com/intelligent-control-lab/ModelVerification.jl/actions/workflows/CI.yml/badge.svg)](https://github.com/intelligent-control-lab/ModelVerification.jl/actions/workflows/CI.yml)
[![Doc](https://github.com/intelligent-control-lab/ModelVerification.jl/actions/workflows/documentation.yml/badge.svg)](https://intelligent-control-lab.github.io/ModelVerification.jl/dev/)
[![codecov](https://codecov.io/github/intelligent-control-lab/ModelVerification.jl/graph/badge.svg?token=W0RYF98CVS)](https://codecov.io/github/intelligent-control-lab/ModelVerification.jl)

## Introduction
Deep Neural Network (DNN) is crucial in approximating nonlinear functions across diverse applications, such as computer vision and control. Verifying specific input-output properties can be a highly challenging task. To this end, we present [ModelVerification.jl](https://github.com/intelligent-control-lab/ModelVerification.jl), the only cutting-edge toolbox that contains a suite of state-of-the-art methods for verifying DNNs. This toolbox significantly extends and improves the previous version ([NeuralVerification.jl](https://sisl.github.io/NeuralVerification.jl/latest/)) and is designed to empower developers and machine learning practioners with robust tools for verifying and ensuring the trustworthiness of their DNN models.
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howpublished = {\url{https://github.com/intelligent-control-lab/ModelVerification.jl}},
year = {2024}
}
```
```

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