280 citations · 1.4k across the 75 of their papers we have counts for
6 papers · 1 filter
Achieving Robustness in the Wild via Adversarial Mixing with Disentangled Representations
Sven Gowal, Chongli Qin, Po-Sen Huang +4
Recent research has made the surprising finding that state-of-the-art deep learning models sometimes fail to generalize to small variations of the input. Adversarial training has b…
Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation
Po-Sen Huang, Robert Stanforth, Johannes Welbl +5
Neural networks are part of many contemporary NLP systems, yet their empirical successes come at the price of vulnerability to adversarial attacks. Previous work has used adversari…
Robust Feasibility of Systems of Quadratic Equations Using Topological Degree Theory
Krishnamurthy Dvijotham, Bala Krishnamoorthy, Yunqi Luo +1
We consider the problem of measuring the margin of robust feasibility of solutions to a system of nonlinear equations. We study the special case of a system of quadratic equations,…
Adversarial Robustness through Local Linearization
Chongli Qin, James Martens, Sven Gowal +6
Adversarial training is an effective methodology for training deep neural networks that are robust against adversarial, norm-bounded perturbations. However, the computational cost…
Knowing When to Stop: Evaluation and Verification of Conformity to Output-size Specifications
Chenglong Wang, Rudy Bunel, Krishnamurthy Dvijotham +3
Models such as Sequence-to-Sequence and Image-to-Sequence are widely used in real world applications. While the ability of these neural architectures to produce variable-length out…
Verification of Non-Linear Specifications for Neural Networks
Chongli Qin, Krishnamurthy, Dvijotham +7
Prior work on neural network verification has focused on specifications that are linear functions of the output of the network, e.g., invariance of the classifier output under adve…