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State-of-the art vision models can achieve superhuman performance on image classification tasks when testing and training data come from the same distribution.
Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-… hook
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Cubuk, E. D., Zoph, B., Schoenholz, S. S., and Le, Q. V · 2017
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Unsupervised learning via meta-learning
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Gpipe: Efficient training of giant neural networks using pipeline parallelism
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Aggregated momentum: Stability through passive damping
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Understanding and correcting pathologies in the training of learned optimizers
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Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., and Le, Q. V · 2018
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Adversarial examples that fool both computer vision and time-limited humans
Elsayed, G., Shankar, S., Cheung, B., Papernot, N., Kurakin, A., Goodfellow, I., and Sohl-Dickstein, J · 2018
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Adversarial vulnerability for any classifier
Fawzi, A., Fawzi, H., and Fawzi, O · 2018
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Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., and Brendel, W · 2018
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Rosenfeld, A., Zemel, R., and Tsotsos, J. K · 2018
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Learned optimizers that scale and generalize
Wichrowska, O., Maheswaranathan, N., Hoffman, M. W., Colmenarejo, S. G., Denil, M., de Freitas, N., and Sohl-Dickstein, J · 2018
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Adversarial examples are a natural consequence of test error in noise
Ford, N., Gilmer, J., Carlini, N., and Cubuk, D · 2019
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Benchmarking neural network robustness to common corruptions and surface variations
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