33 citations · 88 across the 17 of their papers we have counts for
4 papers · 1 filter
CodeMark: Imperceptible Watermarking for Code Datasets against Neural Code Completion Models
Zhensu Sun, Xiaoning Du, Fu Song +1
Code datasets are of immense value for training neural-network-based code completion models, where companies or organizations have made substantial investments to establish and pro…
A Comprehensive Empirical Study of Bugs in Open-Source Federated Learning Frameworks
Weijie Shao, Yuyang Gao, Fu Song +3
Federated learning (FL) is a distributed machine learning (ML) paradigm, allowing multiple clients to collaboratively train shared machine learning (ML) models without exposing cli…
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…