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Reweighting adversarial data during training has been recently shown to improve adversarial robustness, where data closer to the current decision boundaries are regarded as more critical and given larger weights.
A method for solving the convex programming problem with convergence rate O(1/kˆ2)
Yurii Nesterov · 1983
Earlier work this paper cites.
Dynamic learning rate optimization of the backpropagation algorithm
Xiao-Hu Yu, Guo-An Chen, and Shixin Cheng · 1995
Earlier work this paper cites.
Empirical margin distributions and bounding the generalization error of combined classifiers
Vladimir Koltchinskii and Dmitry Panchenko · 2002
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Reading digits in natural images with unsupervised feature learning
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Arjun Nitin Bhagoji, Warren He, Bo Li, and Dawn Song · 2018
Earlier work this paper cites.
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Earlier work this paper cites.
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Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard, and Stefano Soatto · 2018
Earlier work this paper cites.
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Earlier work this paper cites.
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Projection & probability-driven black-box attack
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