76 citations · 94 across the 5 of their papers we have counts for
5 papers
PEA: Enhancing LLM Performance on Computational-Reasoning Tasks
Zi Wang, Shiwei Weng, Mohannad Alhanahnah +2
Large Language Models (LLMs) have exhibited remarkable capabilities across diverse domains, prompting investigations into their potential as generic reasoning engines. While recent…
Rethinking Diversity in Deep Neural Network Testing
Zi Wang, Jihye Choi, Ke Wang +1
Motivated by the success of traditional software testing, numerous diversity measures have been proposed for testing deep neural networks (DNNs). In this study, we propose a shift…
Efficient Symbolic Reasoning for Neural-Network Verification
Zi Wang, Somesh Jha, Krishnamurthy +1
The neural network has become an integral part of modern software systems. However, they still suffer from various problems, in particular, vulnerability to adversarial attacks. In…
Interval Universal Approximation for Neural Networks
Zi Wang, Aws Albarghouthi, Gautam Prakriya +1
To verify safety and robustness of neural networks, researchers have successfully applied abstract interpretation, primarily using the interval abstract domain. In this paper, we s…
Semantic Robustness of Models of Source Code
Goutham Ramakrishnan, Jordan Henkel, Zi Wang +3
Deep neural networks are vulnerable to adversarial examples - small input perturbations that result in incorrect predictions. We study this problem for models of source code, where…