116 citations · 254 across the 9 of their papers we have counts for
9 papers
Convex Bounds on the Softmax Function with Applications to Robustness Verification
Dennis Wei, Haoze Wu, Min Wu +3
The softmax function is a ubiquitous component at the output of neural networks and increasingly in intermediate layers as well. This paper provides convex lower bounds and concave…
VeriX: Towards Verified Explainability of Deep Neural Networks
Min Wu, Haoze Wu, Clark Barrett
We present VeriX (Verified eXplainability), a system for producing optimal robust explanations and generating counterfactuals along decision boundaries of machine learning models.…
Assessing Robustness of Text Classification through Maximal Safe Radius Computation
Emanuele La Malfa, Min Wu, Luca Laurenti +3
Neural network NLP models are vulnerable to small modifications of the input that maintain the original meaning but result in a different prediction. In this paper, we focus on rob…
Robustness Guarantees for Deep Neural Networks on Videos
Min Wu, Marta Kwiatkowska
The widespread adoption of deep learning models places demands on their robustness. In this paper, we consider the robustness of deep neural networks on videos, which comprise both…
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability
Xiaowei Huang, Daniel Kroening, Wenjie Ruan +5
In the past few years, significant progress has been made on deep neural networks (DNNs) in achieving human-level performance on several long-standing tasks. With the broader deplo…
A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees
Min Wu, Matthew Wicker, Wenjie Ruan +2
Despite the improved accuracy of deep neural networks, the discovery of adversarial examples has raised serious safety concerns. In this paper, we study two variants of pointwise r…
Concolic Testing for Deep Neural Networks
Youcheng Sun, Min Wu, Wenjie Ruan +3
Concolic testing combines program execution and symbolic analysis to explore the execution paths of a software program. This paper presents the first concolic testing approach for…
Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the Norm
Wenjie Ruan, Min Wu, Youcheng Sun +3
Deployment of deep neural networks (DNNs) in safety- or security-critical systems requires provable guarantees on their correct behaviour. A common requirement is robustness to adv…
Safety Verification of Deep Neural Networks
Xiaowei Huang, Marta Kwiatkowska, Sen Wang +1
Deep neural networks have achieved impressive experimental results in image classification, but can surprisingly be unstable with respect to adversarial perturbations, that is, min…