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It has been observed that certain loss functions can render deep-learning pipelines robust against flaws in the data.
The best constants in the Khintchine inequality
U. Haagerup · 1981
Earlier work this paper cites.
On the method of bounded differences
C. McDiarmid · 1989
Earlier work this paper cites.
A simple weight decay can improve generalization
A. Krogh and J. Hertz · 1991
Earlier work this paper cites.
A model of multiplicative neural responses in parietal cortex
E. Salinas and L. Abbott · 1996
Earlier work this paper cites.
The sample complexity of pattern classification with neural networks: The size of the weights is more important than the size of the network
P. Bartlett · 1998
Earlier work this paper cites.
On the piecewise analysis of networks of linear threshold neurons
R. Hahnloser · 1998
Earlier work this paper cites.
Rademacher penalties and structural risk minimization
V. Koltchinskii · 2001
Earlier work this paper cites.
Rademacher and Gaussian complexities: risk bounds and structural results
P. Bartlett and S. Mendelson · 2002
Earlier work this paper cites.
Model selection and error estimation
P. Bartlett, S. Boucheron, and G. Lugosi · 2002
Earlier work this paper cites.
Empirical margin distributions and bounding the generalization error of combined classifiers
V. Koltchinskii and D. Panchenko · 2002
Earlier work this paper cites.
Neural network learning: theoretical foundations
M. Anthony and P. Bartlett · 2009
Earlier work this paper cites.
Robust Statistics
P. Huber and E. Ronchetti · 2009
Earlier work this paper cites.
Adversarial examples: attacks and defenses for deep learning
X. Yuan, P. He, Q. Zhu, and X. Li · 2009
Earlier work this paper cites.
Probability: theory and examples
R. Durrett · 2010
Earlier work this paper cites.
The changing history of robustness
S. Stigler · 2010
Earlier work this paper cites.
Robust statistics: the approach based on influence functions
F. Hampel, E. Ronchetti, P. Rousseeuw, and W. Stahel · 2011
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. Hinton · 2012
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A robust, adaptive M-estimator for pointwise estimation in heteroscedastic regression
M. Chichignoud and J. Lederer · 2014
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New concentration inequalities for suprema of empirical processes
J. Lederer and S. van de Geer · 2014
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Robust optimization for deep regression
V. Belagiannis, C. Rupprecht, G. Carneiro, and N. Navab · 2015
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Norm-based capacity control in neural networks
B. Neyshabur, R. Tomioka, and N. Srebro · 2015
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Universal adversarial perturbations
S.-M. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard · 2017
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Ensemble adversarial training: attacks and defenses
F. Tramér, A. Kurakin, N. Papernot, I. Goodfellow, D. Boneh, and P. McDaniel · 2017
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Threat of adversarial attacks on deep learning in computer vision: a survey
N. Akhtar and A. Mian · 2018
Later among the works it cites.
Mentornet: learning data-driven curriculum for very deep neural networks on corrupted labels
L. Jiang, Z. Zhou, T. Leung, L.-J. Li, and L. Fei-Fei · 2018
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Scale calibration for high-dimensional robust regression
P.-L. Loh · 2018
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Distillation as a defense to adversarial perturbations against deep neural networks
N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami · 2015
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Concentration inequalities: a nonasymptotic theory of independence
S. Boucheron, G. Lugosi, and P. Massart · 2016
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Adversarial examples in the physical world
A. Kurakin, I. Goodfellow, and S. Bengio · 2016
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Accessorize to a crime: real and stealthy attacks on state-of-the-art face recognition
M. Sharif, S. Bhagavatula, L. Bauer, and M. Reiter · 2016
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Studying very low resolution recognition using deep networks
Z. Wang, S. Chang, Y. Yang, D. Liu, and T. Huang · 2016
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Delving into adversarial attacks on deep policies
J. Kos and D. Song · 2017
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Foundations of machine learning
M. Mohri, A. Rostamizadeh, and A. Talwalkar · 2018
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Towards robust deep neural networks
T. Wang, Y. Gu, D. Mehta, X. Zhao, and E. Bernal · 2018
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A general and adaptive robust loss function
J. Barron · 2019
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Experimental security research of tesla autopilot, 2019
Tencent Keen Security Lab · 2019
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Provably robust deep learning via adversarially trained smoothed classifiers
H. Salman, G. Yang, J. Li, P. Zhang, H. Zhang, I. Razenshteyn, and S. Bubeck · 2019
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A direct approach to robust deep learning using adversarial networks
H. Wang and C.-N. Yu · 2019
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Size-independent sample complexity of neural networks
N. Golowich, A. Rakhlin, and O. Shamir · 2020
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Nonparametric regression using deep neural networks with ReLU activation function
J. Schmidt-Hieber · 2020
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Statistical guarantees for regularized neural networks
M. Taheri, F. Xie, and J. Lederer · 2020
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