107 citations · 327 across the 11 of their papers we have counts for
12 papers
Demystifying Dependency Bugs in Deep Learning Stack
Kaifeng Huang, Bihuan Chen, Susheng Wu +3
Deep learning (DL) applications, built upon a heterogeneous and complex DL stack (e.g., Nvidia GPU, Linux, CUDA driver, Python runtime, and TensorFlow), are subject to software and…
Towards Understanding Quality Challenges of the Federated Learning for Neural Networks: A First Look from the Lens of Robustness
Amin Eslami Abyane, Derui Zhu, Roberto Souza +2
Federated learning (FL) is a distributed learning paradigm that preserves users' data privacy while leveraging the entire dataset of all participants. In FL, multiple models are tr…
xFuzz: Machine Learning Guided Cross-Contract Fuzzing
Yinxing Xue, Jiaming Ye, Wei Zhang +4
Smart contract transactions are increasingly interleaved by cross-contract calls. While many tools have been developed to identify a common set of vulnerabilities, the cross-contra…
DeepMix: Online Auto Data Augmentation for Robust Visual Object Tracking
Ziyi Cheng, Xuhong Ren, Felix Juefei-Xu +4
Online updating of the object model via samples from historical frames is of great importance for accurate visual object tracking. Recent works mainly focus on constructing effecti…
Countering Malicious DeepFakes: Survey, Battleground, and Horizon
Felix Juefei-Xu, Run Wang, Yihao Huang +3
The creation or manipulation of facial appearance through deep generative approaches, known as DeepFake, have achieved significant progress and promoted a wide range of benign and…
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…
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…
Machine Learning Testing: Survey, Landscapes and Horizons
Jie M. Zhang, Mark Harman, Lei Ma +1
This paper provides a comprehensive survey of Machine Learning Testing (ML testing) research. It covers 144 papers on testing properties (e.g., correctness, robustness, and fairnes…
DeepCruiser: Automated Guided Testing for Stateful Deep Learning Systems
Xiaoning Du, Xiaofei Xie, Yi Li +3
Deep learning (DL) defines a data-driven programming paradigm that automatically composes the system decision logic from the training data. In company with the data explosion and h…
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…
Metamorphic Relation Based Adversarial Attacks on Differentiable Neural Computer
Alvin Chan, Lei Ma, Felix Juefei-Xu +3
Deep neural networks (DNN), while becoming the driving force of many novel technology and achieving tremendous success in many cutting-edge applications, are still vulnerable to ad…
Combinatorial Testing for Deep Learning Systems
Lei Ma, Fuyuan Zhang, Minhui Xue +4
Deep learning (DL) has achieved remarkable progress over the past decade and been widely applied to many safety-critical applications. However, the robustness of DL systems recentl…