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With the increasing adoption of AI, inherent security and privacy vulnerabilities formachine learning systems are being discovered.
Fast exact multiplication by the hessian
B. A. Pearlmutter · 1994
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Differential privacy
C. Dwork · 2006
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Introduction to Semi-Supervised Learning
X. Zhu, A. B. Goldberg, R. Brachman, and T. Dietterich · 2009
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2014
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Model inversion attacks that exploit confidence information and basic countermeasures
M. Fredrikson, S. Jha, and T. Ristenpart · 2015
Cited alongside, same era.
Understanding deep image representations by inverting them
A. Mahendran and A. Vedaldi · 2015
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks, 2015
A. Radford, L. Metz, and S. Chintala · 2016
Cited alongside, same era.
Membership inference attacks against machine learning models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
Cited alongside, same era.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
H. Xiao, K. Rasul, and R. Vollgraf · 2017
Later among the works it cites.
Towards effective GANs for data distributions with diverse modes, 2018
S. Agrawal, G. Singh, and M. Khapra · 2018
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A style-based generator architecture for generative adversarial networks
T. Karras, S. Laine, and T. Aila · 2018
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Disconnected manifold learning for generative adversarial networks
M. Khayatkhoei, A. Elgammal, and M. Singh · 2018
Later among the works it cites.
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