Machine Unlearning, A Comparative Analysis
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-62495-7_42
Reference17 articles.
1. Carlini, N., Chien, S., Nasr, M., Song, S., Terzis, A., Tramer, F.: Membership inference attacks from first principles. In: 2022 IEEE Symposium on Security and Privacy (SP), pp. 1897–1914. IEEE (2022)
2. Chen, M., Gao, W., Liu, G., Peng, K., Wang, C.: Boundary unlearning: rapid forgetting of deep networks via shifting the decision boundary. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7766–7775 (2023)
3. Doughan, Z., Al Mubasher, H., Kassem, R., El-Hajj, A.M., Haidar, A.M., Sliman, L.: Logic-based neural network for pattern correction. In: 2022 International Conference on Smart Systems and Power Management (IC2SPM), pp. 52–57. IEEE (2022)
4. Doughan, Z., Kassem, R., El-Hajj, A.M., Haidar, A.M.: Logic-based neural network for image compression applications. In: 2021 3rd IEEE Middle East and North Africa Communications Conference (MENACOMM), pp. 92–97. IEEE (2021)
5. Doughan, Z., Kassem, R., El-Hajj, A.M., Haidar, A.M.: Novel preprocessors for convolution neural networks. IEEE Access 10, 36834–36845 (2022)
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