NeuRecover: Regression-Controlled Repair of Deep Neural Networks with Training History

Author:

Tokui Shogo1,Tokumoto Susumu1,Yoshii Akihito1,Ishikawa Fuyuki2,Nakagawa Takao1,Munakata Kazuki1,Kikuchi Shinji1

Affiliation:

1. Fujitsu Limited,Kawasaki,Japan

2. National Institute of Informatics,Tokyo,Japan

Publisher

IEEE

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. How Do Deep Learning Faults Affect AI-Enabled Cyber-Physical Systems in Operation? A Preliminary Study Based on DeepCrime Mutation Operators;2023 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM);2023-10-26

2. ArchRepair : Block-Level Architecture-Oriented Repairing for Deep Neural Networks;ACM Transactions on Software Engineering and Methodology;2023-07-24

3. Adaptive Search-based Repair of Deep Neural Networks;Proceedings of the Genetic and Evolutionary Computation Conference;2023-07-12

4. Towards Formal Repair and Verification of Industry-scale Deep Neural Networks;2023 IEEE/ACM 45th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion);2023-05

5. An Initial Analysis of Repair and Side-effect Prediction for Neural Networks;2023 IEEE/ACM 2nd International Conference on AI Engineering – Software Engineering for AI (CAIN);2023-05

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