10 citations · 10 across the 6 of their papers we have counts for
7 papers
Branch and Bound for Relational Verification of Neural Networks
Kota Fukuda, Zhenya Zhang, Guanqin Zhang +1
Verification of neural networks against relational specifications, such as global robustness, is crucial for safety-critical applications of cyber-physical systems (CPS), given the…
Mining Verdict Boundaries for Neural Network Verification
Jiawei Ren, Guanqin Zhang, Zhenya Zhang +1
Branch and Bound (BaB) aims to achieve complete verification of neural networks by adaptively partitioning the problem and applying off-the-shelf verifiers to subproblems. Its prob…
Tensor-Based Batch Fuzzing with Adaptive Perturbation Scaling for Deep Neural Networks
Guanqin Zhang, Yulei Sui
Deep neural networks are increasingly deployed in safety-critical domains such as autonomous driving and medical diagnosis, yet their opaque, high-dimensional parameter spaces make…
Evaluating LLM Personalization via Semantic Constraint Verification
Xuran Li, Guanqin Zhang, Imran Razzak +4
Current evaluation paradigms for Large Language Model (LLM) personalization rely heavily on brittle surface-matching metrics or computationally expensive LLM-as-a-judge protocols,…
Efficient Neural Network Verification via Order Leading Exploration of Branch-and-Bound Trees
Guanqin Zhang, Kota Fukuda, Zhenya Zhang +4
The vulnerability of neural networks to adversarial perturbations has necessitated formal verification techniques that can rigorously certify the quality of neural networks. As the…
Adaptive Branch-and-Bound Tree Exploration for Neural Network Verification
Kota Fukuda, Guanqin Zhang, Zhenya Zhang +2
Formal verification is a rigorous approach that can provably ensure the quality of neural networks, and to date, Branch and Bound (BaB) is the state-of-the-art that performs verifi…