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Transfer learning is widely used for transferring knowledge from a source domain to the target domain where the labeled data is scarce.
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Adversarial Multi-task Learning for Text Classification. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017
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Property inference attacks on fully connected neural networks using permutation invariant representations. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, CCS 2018
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Generalization Bounds of SGLD for Non-convex Learning: Two Theoretical Viewpoints. In Conference On Learning Theory, COLT 2018
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A Survey on Deep Transfer Learning. In ICANN 2018 - 27th International Conference on Artificial Neural Networks
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Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning. In 2019 IEEE Symposium on Security and Privacy, SP 2019
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ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models. In 26th Annual Network and Distributed System Security Symposium, NDSS 2019
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Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics
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A Comprehensive Survey on Transfer Learning
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P3SGD: Patient Privacy Preserving SGD for Regularizing Deep CNNs in Pathological Image Classification. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019
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