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Machine learning is vulnerable to a wide variety of attacks.
A stochastic approximation method
H. Robbins and S. Monro · 1951
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Some methods of speeding up the convergence of iteration methods
B. Polyak · 1964
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A Note on Quantiles in Large Samples
R. R. Bahadur · 1966
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Curriculum learning
Y. Bengio, J. Louradour, R. Collobert, and J. Weston · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky, G. Hinton, et al · 2009
Earlier work this paper cites.
Evasion attacks against machine learning at test time
B. Biggio, I. Corona, D. Maiorca, B. Nelson, N. Šrndić, P. Laskov, G. Giacinto, and F. Roli · 2013
Earlier work this paper cites.
Accelerating stochastic gradient descent using predictive variance reduction
R. Johnson and T. Zhang · 2013
Earlier work this paper cites.
On the importance of initialization and momentum in deep learning
I. Sutskever, J. Martens, G. Dahl, and G. Hinton · 2013
Earlier work this paper cites.
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples, 2015
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
Cited alongside, same era.
Character-level convolutional networks for text classification
X. Zhang, J. J. Zhao, and Y. LeCun · 2015
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Practical coreset constructions for machine learning, 2017
O. Bachem, M. Lucic, and A. Krause · 2017
Cited alongside, same era.
Targeted backdoor attacks on deep learning systems using data poisoning
X. Chen, C. Liu, B. Li, K. Lu, and D. Song · 2017
Cited alongside, same era.
Badnets: Identifying vulnerabilities in the machine learning model supply chain
Poison frogs! targeted clean-label poisoning attacks on neural networks
A. Shafahi, W. R. Huang, M. Najibi, O. Suciu, C. Studer, T. Dumitras, and T. Goldstein · 2018
Later among the works it cites.
Towards deep learning models resistant to adversarial attacks, 2019
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2019
Later among the works it cites.
A survey on bias and fairness in machine learning
N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, and A. Galstyan · 2019
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Security Engineering
R. Anderson · 2020
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On the security relevance of initial weights in deep neural networks
K. Grosse, T. A. Trost, M. Mosbach, M. Backes, and D. Klakow · 2020
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T. Gu, B. Dolan-Gavitt, and S. Garg · 2017
Cited alongside, same era.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam · 2017
Cited alongside, same era.
Practical black-box attacks against machine learning
N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami · 2017
Cited alongside, same era.
Bias on the web
R. Baeza-Yates · 2018
Cited alongside, same era.
Manipulating machine learning: Poisoning attacks and countermeasures for regression learning
M. Jagielski, A. Oprea, B. Biggio, C. Liu, C. Nita-Rotaru, and B. Li · 2018
Cited alongside, same era.
Chapter 2. order statistics
H. Chen
Cited in the paper.
Dynamic backdoor attacks against machine learning models, 2020
A. Salem, R. Wen, M. Backes, S. Ma, and Y. Zhang · 2020
Later among the works it cites.
Poisoned classifiers are not only backdoored, they are fundamentally broken, 2020
M. Sun, S. Agarwal, and J. Z. Kolter · 2020
Later among the works it cites.
Moving beyond “algorithmic bias is a data problem”
S. Hooker · 2021
Closest in time.
Sponge examples: Energy-latency attacks on neural networks
I. Shumailov, Y. Zhao, D. Bates, N. Papernot, R. Mullins, and R. Anderson · 2021
Closest in time.
On the origin of implicit regularization in stochastic gradient descent, 2021
S. L. Smith, B. Dherin, D. G. T. Barrett, and S. De · 2021
Closest in time.