33 citations · 88 across the 17 of their papers we have counts for
6 papers · 1 filter
QVIP: An ILP-based Formal Verification Approach for Quantized Neural Networks
Yedi Zhang, Zhe Zhao, Fu Song +3
Deep learning has become a promising programming paradigm in software development, owing to its surprising performance in solving many challenging tasks. Deep neural networks (DNNs…
QEBVerif: Quantization Error Bound Verification of Neural Networks
Yedi Zhang, Fu Song, Jun Sun
To alleviate the practical constraints for deploying deep neural networks (DNNs) on edge devices, quantization is widely regarded as one promising technique. It reduces the resourc…
Don't Complete It! Preventing Unhelpful Code Completion for Productive and Sustainable Neural Code Completion Systems
Zhensu Sun, Xiaoning Du, Fu Song +4
Currently, large pre-trained language models are widely applied in neural code completion systems. Though large code models significantly outperform their smaller counterparts, aro…
Abstraction and Refinement: Towards Scalable and Exact Verification of Neural Networks
Jiaxiang Liu, Yunhan Xing, Xiaomu Shi +3
As a new programming paradigm, deep neural networks (DNNs) have been increasingly deployed in practice, but the lack of robustness hinders their applications in safety-critical dom…
Towards Understanding and Mitigating Audio Adversarial Examples for Speaker Recognition
Guangke Chen, Zhe Zhao, Fu Song +4
Speaker recognition systems (SRSs) have recently been shown to be vulnerable to adversarial attacks, raising significant security concerns. In this work, we systematically investig…
AS2T: Arbitrary Source-To-Target Adversarial Attack on Speaker Recognition Systems
Guangke Chen, Zhe Zhao, Fu Song +3
Recent work has illuminated the vulnerability of speaker recognition systems (SRSs) against adversarial attacks, raising significant security concerns in deploying SRSs. However, t…