activity
20182022
most citedTowards Characterizing Adversarial Defects of Deep Learning Software from the Lens of Uncertainty

5 citations · 7 across the 2 of their papers we have counts for

collaborators

5 papers

cs.SE20222 cited

GraphCode2Vec: Generic Code Embedding via Lexical and Program Dependence Analyses

Wei Ma, Mengjie Zhao, Ezekiel Soremekun +6

Code embedding is a keystone in the application of machine learning on several Software Engineering (SE) tasks. To effectively support a plethora of SE tasks, the embedding needs t…

cs.SE20205 cited

Towards Characterizing Adversarial Defects of Deep Learning Software from the Lens of Uncertainty

Xiyue Zhang, Xiaofei Xie, Lei Ma +5

Over the past decade, deep learning (DL) has been successfully applied to many industrial domain-specific tasks. However, the current state-of-the-art DL software still suffers fro…

cs.LG2019

An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms

Qianyu Guo, Sen Chen, Xiaofei Xie +6

Deep Learning (DL) has recently achieved tremendous success. A variety of DL frameworks and platforms play a key role to catalyze such progress. However, the differences in archite…

cs.LG2018

An Orchestrated Empirical Study on Deep Learning Frameworks and Platforms

Qianyu Guo, Xiaofei Xie, Lei Ma +6

Deep learning (DL) has recently achieved tremendous success in a variety of cutting-edge applications, e.g., image recognition, speech and natural language processing, and autonomo…

cs.SE2018

Secure Deep Learning Engineering: A Software Quality Assurance Perspective

Lei Ma, Felix Juefei-Xu, Minhui Xue +7

Over the past decades, deep learning (DL) systems have achieved tremendous success and gained great popularity in various applications, such as intelligent machines, image processi…