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Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

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Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

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Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks.

Adversarial training with informed data selection

Robust Binary Models by Pruning Randomly-initialized Networks

Loss landscape and symmetries in Neural Networks

From Human-Designed Convolutional Neural Networks Towards Robust Neural Architecture Search

POSEIDON: Privacy-Preserving Federated Neural Network Learning

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Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness

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

Understanding and Improving Fast Adversarial Training