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T. W. Anderson and D. A. Darling, “Asymptotic theory of certain “goodness of fit” criteria based on stochastic processes,” The annals of mathematical statistics , 1952
1952
Earlier work this paper cites.
S. S. Shapiro and M. B. Wilk, “An analysis of variance test for normality (complete samples),” Biometrika , vol. 52, no. 3/4, 1965
1965
Earlier work this paper cites.
R. B. d’Agostino, “An omnibus test of normality for moderate and large size samples,” Biometrika , vol. 58, no. 2, 1971
1971
Earlier work this paper cites.
D. Angluin, “Queries and concept learning,” Machine learning , vol. 2, no. 4, 1988
1988
Earlier work this paper cites.
Y. LeCun, B. E. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. E. Hubbard, and L. D. Jackel, “Handwritten digit recognition with a back-propagation network,” in Proceedings of NIPS , 1990
1990
Earlier work this paper cites.
G. G. Towell and J. W. Shavlik, “Extracting refined rules from knowledge-based neural networks,” Machine learning , vol. 13, no. 1, 1993
1993
Earlier work this paper cites.
D. Cohn, L. Atlas, and R. Ladner, “Improving generalization with active learning,” Machine learning , vol. 15, no. 2, 1994
1994
Earlier work this paper cites.
N. H. Bshouty, R. Cleve, R. Gavaldà, S. Kannan, and C. Tamon, “Oracles and queries that are sufficient for exact learning,” Journal of Computer and System Sciences , vol. 52, no. 3, 1996
1996
Earlier work this paper cites.
M. Craven and J. W. Shavlik, “Extracting tree-structured representations of trained networks,” in Proceedings of NIPS , 1996
1996
Earlier work this paper cites.
L. A. S. Sors and L. A. Santaló, Integral geometry and geometric probability . Cambridge university press, 2004
2004
Earlier work this paper cites.
D. Lowd and C. Meek, “Adversarial learning,” in Proceedings of ACM SIGKDD , 2005
2005
Earlier work this paper cites.
C. Bucilua, R. Caruana, and A. Niculescu-Mizil, “Model compression,” in Proceedings of ACM SIGKDD , 2006
2006
Earlier work this paper cites.
J. Philip, The probability distribution of the distance between two random points in a box . KTH mathematics, Royal Institute of Technology, 2007
2007
Earlier work this paper cites.
R. Timofte, K. Zimmermann, and L. van Gool, “Multi-view traffic sign detection, recognition, and 3d localisation,” in IEEE Computer Society Workshop on Application of Computer Vision , 2009
2009
Earlier work this paper cites.
Y. LeCun, C. Cortes, and C. Burges, “Mnist handwritten digit database,” AT&T Labs , 2010
2010
Earlier work this paper cites.
N. M. Razali, Y. B. Wah et al. , “Power comparisons of shapiro-wilk, kolmogorov-smirnov, lilliefors and anderson-darling tests,” Journal of statistical modeling and analytics , vol. 2, no. 1, 2011
2011
Earlier work this paper cites.
J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel, “The german traffic sign recognition benchmark: a multi-class classification competition,” in IEEE International Joint Conference on Neural Networks , 2011
2011
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proceedings of NIPS , 2012
2012
Earlier work this paper cites.
K. Murphy, “Machine learning: a probabilistic approach,” Massachusetts Institute of Technology , 2012
2012
Earlier work this paper cites.
B. Nelson, B. I. Rubinstein, L. Huang, A. D. Joseph, S. J. Lee, S. Rao, and J. Tygar, “Query strategies for evading convex-inducing classifiers,” Journal of Machine Learning Research , vol. 13, no. May, 2012
2012
Cited alongside, same era.
J. Snoek, H. Larochelle, and R. P. Adams, “Practical bayesian optimization of machine learning algorithms,” in Proceedings of NIPS , 2012
2012
Cited alongside, same era.
D. Stevens and D. Lowd, “On the hardness of evading combinations of linear classifiers,” in Proceedings of ACM AISec , 2013
2013
Cited alongside, same era.
J. Ekberg, K. Kostiainen, and N. Asokan, “The untapped potential of trusted execution environments on mobile devices,” IEEE Security & Privacy , vol. 12, no. 4, 2014
2014
Cited alongside, same era.
N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami, “Practical black-box attacks against machine learning,” in Proceedings of ACM ASIACCS , 2017
2017
Later among the works it cites.
V. Smith, C.-K. Chiang, M. Sanjabi, and A. S. Talwalkar, “Federated multi-task learning,” in Proceedings of NIPS , 2017
2017
Later among the works it cites.
2017
Later among the works it cites.
Amazon, “Amazon machine learning,” https://aws.amazon.com/aml/ , last accessed 14/11/2018
2018
Closest in time.
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2014
Cited alongside, same era.
N. Šrndic and P. Laskov, “Practical evasion of a learning-based classifier: A case study,” in IEEE Symposium on Security and Privacy , 2014
2014
Cited alongside, same era.
2015
Cited alongside, same era.
I. Goodfellow, Y. Bengio, and A. Courville, Deep learning . MIT press, 2016, vol. 1
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
F. Tramèr, F. Zhang, A. Juels, M. K. Reiter, and T. Ristenpart, “Stealing machine learning models via prediction apis.” in USENIX Security Symposium , 2016
2016
Cited alongside, same era.
2018
Closest in time.
2018
Closest in time.
Y. Dong, F. Liao, T. Pang, H. Su, J. Zhu, X. Hu, and J. Li, “Boosting adversarial attacks with momentum,” in Computer Vision and Pattern Recognition , 2018
2018
Closest in time.
W. Hua, Z. Zhang, and G. E. Suh, “Reverse engineering convolutional neural networks through side-channel information leaks,” in ACM Design Automation Conference , 2018
2018
Closest in time.
Intel, “Movidius myriad x vpu,” https://www.movidius.com/ , last accessed 14/11/2018
2018
Closest in time.
M. Kesarwani, B. Mukhoty, V. Arya, and S. Mehta, “Model extraction warning in mlaas paradigm,” 2018
2018
Closest in time.
2018
Closest in time.
Microsoft, “Azure machine learning,” https://azure.microsoft.com/en-us/overview/machine-learning/ , last accessed 14/11/2018
2018
Closest in time.
S. J. Oh, M. Augustin, M. Fritz, and B. Schiele, “Towards reverse-engineering black-box neural networks,” in International Conference on Learning Representations , 2018
2018
Closest in time.
L. Pengcheng, J. Yi, and L. Zhang, “Query-efficient black-box attack by active learning,” in 2018 IEEE International Conference on Data Mining (ICDM) . IEEE, 2018, pp. 1200–1205
2018
Closest in time.
E. Quiring, D. Arp, and K. Rieck, “Forgotten siblings: Unifying attacks on machine learning and digital watermarking,” in 2018 IEEE European Symposium on Security and Privacy (EuroS&P) , 2018, pp. 488–502
2018
Closest in time.
M. Sharif, L. Bauer, and M. K. Reiter, “On the suitability of lp-norms for creating and preventing adversarial examples,” in Proceedings of IEEE CVPR Workshops , 2018
2018
Closest in time.
B. Wang and N. Zhenqiang Gong, “Stealing Hyperparameters in Machine Learning,” in IEEE Symposium on Security and Privacy , 2018
2018
Closest in time.
T. Orekondy, B. Schiele, and M. Fritz, “Knockoff nets: Stealing functionality of black-box models,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2019
2019
Closest in time.