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This paper revisits the simple, long-studied, yet still unsolved problem of making image classifiers robust to imperceptible perturbations.
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Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
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Scaling laws for neural language models
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and Amodei, D · 2020
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Shankar, V., Roelofs, R., Mania, H., Fang, A., Recht, B., and Schmidt, L · 2020
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Revisiting residual networks for adversarial robustness
Huang, S., Lu, Z., Deb, K., and Boddeti, V. N · 2023
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Baseline defenses for adversarial attacks against aligned language models
Jain, N., Schwarzschild, A., Wen, Y., Somepalli, G., Kirchenbauer, J., Chiang, P.-y., Goldblum, M., Saha, A., Geiping, J., and Goldstein, T · 2023
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Data augmentation can improve robustness
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Adversarially trained neural representations are already as robust as biological neural representations
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Subtle adversarial image manipulations influence both human and machine perception
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Better diffusion models further improve adversarial training
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Key trends and figures in machine learning, 2023
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