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Deep neural networks (DNNs) are powerful nonlinear architectures that are known to be robust to random perturbations of the input.
Backpropagation applied to handwritten zip code recognition
LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., and Jackel, L.D · 1989
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A brief survey of bandwidth selection for density estimation
Jones, M.C., Marron, J.S., and Sheather, S.J · 1996
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Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Rasmussen, C.E. and Williams, C.K.I · 2005
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Nonlinear Dimensionality Reduction
Lee, J.A. and Verleysen, M · 2007
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Better mixing via deep representations
Bengio, Y., Mesnil, G., Dauphin, Y., and Rifai, S · 2013
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Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., and Sutskever, I · 2014
Cited alongside, same era.
A theoretically grounded application of dropout in recurrent neural networks
Gal, Y · 2015
Cited alongside, same era.
Dropout as a Bayesian approximation: representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z · 2015
Cited alongside, same era.
Deep manifold traversal: changing labels with convolutional features
Gardner, J.R., Upchurch, P., Kusner, M.J., Li, Y., Weinberger, K.Q., Bala, K., and Hopcroft, J.E · 2015
Cited alongside, same era.
Explaining and harnessing adversarial examples
Goodfellow, I.J., Shlens, J., and Szegedy, C · 2015
Cited alongside, same era.
Deep learning
LeCun, Y., Bengio, Y., and Hinton, G · 2015
Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D · 2016
Later among the works it cites.
Robustness of classifiers: from adversarial to random noise
Fawzi, A., Moosavi-Dezfooli, S., and Frossard, P · 2016
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A boundary tilting perspective on the phenomenon of adversarial samples
Tanay, T. and Griffin, L · 2016
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Adversarial examples in the physical world
Kurakin, A., Goodfellow, I.J., and Bengio, S · 2017
Closest in time.
On detecting adversarial perturbations
Metzen, J.H., Genewein, T., Fischer, V., and Bischoff, B · 2017
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Cited alongside, same era.
cleverhans v1.0.0: an adversarial machine learning library
Papernot, N., Goodfellow, I.J., Sheatsley, R., Feinman, R., and McDaniel, P
Cited in the paper.
The limitations of deep learning in adversarial settings
Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z.B., and Swami, A
Cited in the paper.
Distillation as a defense to adversarial perturbations against deep neural networks
Papernot, N., McDaniel, P., Wu, X., Jha, S., and Swami, A
Cited in the paper.