10 citations · 12 across the 5 of their papers we have counts for
5 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…
Precise Verification of Transformers through ReLU-Catalyzed Abstraction Refinement
Hengjie Liu, Zhenya Zhang, Jianjun Zhao
Formal verification of transformers has become increasingly important due to their widespread deployment in safety-critical applications. Compared to classic neural networks, the i…
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…
MIXCODE: Enhancing Code Classification by Mixup-Based Data Augmentation
Zeming Dong, Qiang Hu, Yuejun Guo +5
Inspired by the great success of Deep Neural Networks (DNNs) in natural language processing (NLP), DNNs have been increasingly applied in source code analysis and attracted signifi…