1. Abadi, M., et al.: Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, CCS 2016, pp. 308–318. Association for Computing Machinery, New York (2016). https://doi.org/10.1145/2976749.2978318
2. Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138–52160 (2018). https://doi.org/10.1109/ACCESS.2018.2870052
3. Al-Rubaie, M., Chang, J.M.: Privacy-preserving machine learning: threats and solutions. IEEE Secur. Priv. 17(2), 49–58 (2019). https://doi.org/10.1109/MSEC.2018.2888775
4. Anil, C., Lucas, J., Grosse, R.: Sorting out Lipschitz function approximation. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 97, pp. 291–301. PMLR (2019)
5. Arpit, D., et al.: A closer look at memorization in deep networks. In: Precup, D., Teh, Y.W. (eds.) Proceedings of the 34th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 70, pp. 233–242. PMLR (2017). https://proceedings.mlr.press/v70/arpit17a.html