First Three Years of the International Verification of Neural Networks Competition (VNN-COMP)
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866916938175741952 |
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| author | Brix, Christopher Müller, Mark Niklas Bak, Stanley Johnson, Taylor T. Liu, Changliu |
| author_facet | Brix, Christopher Müller, Mark Niklas Bak, Stanley Johnson, Taylor T. Liu, Changliu |
| contents | This paper presents a summary and meta-analysis of the first three iterations of the annual International Verification of Neural Networks Competition (VNN-COMP) held in 2020, 2021, and 2022. In the VNN-COMP, participants submit software tools that analyze whether given neural networks satisfy specifications describing their input-output behavior. These neural networks and specifications cover a variety of problem classes and tasks, corresponding to safety and robustness properties in image classification, neural control, reinforcement learning, and autonomous systems. We summarize the key processes, rules, and results, present trends observed over the last three years, and provide an outlook into possible future developments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_05815 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | First Three Years of the International Verification of Neural Networks Competition (VNN-COMP) Brix, Christopher Müller, Mark Niklas Bak, Stanley Johnson, Taylor T. Liu, Changliu Machine Learning Artificial Intelligence Software Engineering This paper presents a summary and meta-analysis of the first three iterations of the annual International Verification of Neural Networks Competition (VNN-COMP) held in 2020, 2021, and 2022. In the VNN-COMP, participants submit software tools that analyze whether given neural networks satisfy specifications describing their input-output behavior. These neural networks and specifications cover a variety of problem classes and tasks, corresponding to safety and robustness properties in image classification, neural control, reinforcement learning, and autonomous systems. We summarize the key processes, rules, and results, present trends observed over the last three years, and provide an outlook into possible future developments. |
| title | First Three Years of the International Verification of Neural Networks Competition (VNN-COMP) |
| topic | Machine Learning Artificial Intelligence Software Engineering |
| url | https://arxiv.org/abs/2301.05815 |