Conclusion
This letter introduces an approach to accelerate constraint-based neural network repairs by example prioritization and selection. The experiments demonstrate the effectiveness of our approach in accelerating constraint-based neural network repairs. Different training methods may lead to changes in the data in Table 1. We repeated the experiment three times, and the data of Table 1 changed very little. In the future, we will explore the effectiveness of the sample selection strategy on other training methods, neural networks, repair approaches, and datasets.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant No. 62132020), and the Major Project of ISCAS (ISCAS-ZD-202302).
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Zhang, L., Sun, S., Yan, J. et al. Accelerating constraint-based neural network repairs by example prioritization and selection. Front. Comput. Sci. 19, 194332 (2025). https://doi.org/10.1007/s11704-024-3902-x
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DOI: https://doi.org/10.1007/s11704-024-3902-x