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Rethinking data augmentation for adversarial robustness

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Rethinking data augmentation for adversarial robustness

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On the robustness of randomized classifiers to adversarial examples

Controlling the Complexity and Lipschitz Constant improves Polynomial Nets

Adversarial Robustness by Design Through Analog Computing And Synthetic Gradients

Watermarking-based Defense against Adversarial Attacks on Deep Neural Networks

Predicting optical transmission through complex scattering media from reflection patterns with deep neural networks

Supplementary Material - AL2: Progressive Activation Loss for Learning General Representations in Classification Neural Networks

Disentangling feature and lazy training in deep neural networks

Exact Preimages of Neural Network Aircraft Collision Avoidance Systems

The Unstoppable Rise of Computational Linguistics in Deep Learning

From Classical to Unsupervised-Deep-Learning Methods for Solving Inverse Problems in Imaging.