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Data augmentation (DA) is commonly used during model training, as it significantly improves test error and model robustness.
A problem in geometric probability
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Training with noise is equivalent to Tikhonov regularization
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Certifying some distributional robustness with principled adversarial training
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Provable defenses against adversarial examples via the convex outer adversarial polytope
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Adversarial examples are a natural consequence of test error in noise
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