Search Results for author:
Found 6 papers, 2 papers with code
Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification
The reliability of neural networks is essential for their use in safety-critical applications.
Exploring Misclassifications of Robust Neural Networks to Enhance Adversarial Attacks
Progress in making neural networks more robust against adversarial attacks is mostly marginal, despite the great efforts of the research community.
CLIP: Cheap Lipschitz Training of Neural Networks
Despite the large success of deep neural networks (DNN) in recent years, most neural networks still lack mathematical guarantees in terms of stability.
Identifying Untrustworthy Predictions in Neural Networks by Geometric Gradient Analysis
The susceptibility of deep neural networks to untrustworthy predictions, including out-of-distribution (OOD) data and adversarial examples, still prevent their widespread use in safety-critical applications.
Dynamically Sampled Nonlocal Gradients for Stronger Adversarial Attacks
We empirically show that by incorporating this nonlocal gradient information, we are able to give a more accurate estimation of the global descent direction on noisy and non-convex loss surfaces.
Towards Rapid and Robust Adversarial Training with One-Step Attacks
Further, we add a learnable regularization step prior to the neural network, which we call Pixelwise Noise Injection Layer (PNIL).
