← Search

Kai Y. Xiao

2 accepted papers

2019

Evaluating Robustness of Neural Networks with Mixed Integer Programming

ICLR 2019poster

Neural networks trained only to optimize for training accuracy can often be fooled by adversarial examples --- slightly perturbed inputs misclassified with high confidence. Verification of networks enables us to gauge their vulnerability to such adversarial examples. We formulate verification of pie…

2019

Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability

ICLR 2019poster

We explore the concept of co-design in the context of neural network verification. Specifically, we aim to train deep neural networks that not only are robust to adversarial perturbations but also whose robustness can be verified more easily. To this end, we identify two properties of network models…