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Due to numerous breakthroughs in real-world applications brought by machine intelligence, deep neural networks (DNNs) are widely employed in critical applications.
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Imagenet classification with deep convolutional neural networks
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Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus · 2014
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Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Feature squeezing: Detecting adversarial examples in deep neural networks
Weilin Xu, David Evans, and Yanjun Qi · 2018
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Overfitting in adversarially robust deep learning
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Adversarial weight perturbation helps robust generalization
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Attacks which do not kill training make adversarial learning stronger
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Very deep vaes generalize autoregressive models and can outperform them on images
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Highly accurate protein structure prediction with AlphaFold
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Sylvestre-Alvise Rebuffi, Sven Gowal, Dan A Calian, Florian Stimberg, Olivia Wiles, and Timothy Mann · 2021
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Improved protein contact prediction using dimensional hybrid residual networks and singularity enhanced loss function
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Geometry-aware instance-reweighted adversarial training
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