adversarial robustness

Wasserstein Adversarial Examples via Projected Sinkhorn Iterations ICML 2019 You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle NeurIPS 2019 Adversarial Examples that Fool both Computer Vision and Time-Limited Humans NeurIPS 2018 Adversarially Robust Optimization with Gaussian Processes NeurIPS 2018 Adversarial Risk and Robustness: General Definitions and Implications for the Uniform Distribution NeurIPS 2018 Adversarial Risk and the Dangers of Evaluating Against Weak Attacks ICML 2018 Analyzing the Robustness of Nearest Neighbors to Adversarial Examples ICML 2018 A Scalable Laplace Approximation for Neural Networks ICLR 2018 A Simple Cache Model for Image Recognition NeurIPS 2018 A Spectral View of Adversarially Robust Features NeurIPS 2018 Attacking Binarized Neural Networks ICLR 2018 Byzantine Stochastic Gradient Descent NeurIPS 2018 Certified Defenses against Adversarial Examples ICLR 2018 Deep Defense: Training DNNs with Improved Adversarial Robustness NeurIPS 2018 Defense Against Universal Adversarial Perturbations CVPR 2018 Depth-Limited Solving for Imperfect-Information Games NeurIPS 2018 Differentiable Abstract Interpretation for Provably Robust Neural Networks ICML 2018 DRACO: Byzantine-resilient Distributed Training via Redundant Gradients ICML 2018 Efficient and Robust Question Answering from Minimal Context over Documents ACL 2018 Efficient Formal Safety Analysis of Neural Networks NeurIPS 2018 Efficient Neural Network Robustness Certification with General Activation Functions NeurIPS 2018 Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach ICLR 2018 Fast and Effective Robustness Certification NeurIPS 2018 Feature Quantization for Defending Against Distortion of Images CVPR 2018 Hessian-based Analysis of Large Batch Training and Robustness to Adversaries NeurIPS 2018