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Qiang Hu
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An empirical study towards characterizing deep learning development and deployment across different frameworks and platforms
Q Guo, S Chen, X Xie, L Ma, Q Hu, H Liu, Y Liu, J Zhao, X Li
ASE 2019, 2019
892019
DeepMutation++: A mutation testing framework for deep learning systems
Q Hu, L Ma, X Xie, B Yu, Y Liu, J Zhao
ASE 2019, 2019
682019
Towards characterizing adversarial defects of deep learning software from the lens of uncertainty
X Zhang, X Xie, L Ma, X Du, Q Hu, Y Liu, J Zhao, M Sun
ICSE 2020, 2020
552020
Secure deep learning engineering: A software quality assurance perspective
L Ma, F Juefei-Xu, M Xue, Q Hu, S Chen, B Li, Y Liu, J Zhao, J Yin, S See
arXiv preprint arXiv:1810.04538, 2018
292018
Deepgraph: A pycharm tool for visualizing and understanding deep learning models
Q Hu, L Ma, J Zhao
APSEC 2018, 2018
182018
An Empirical Study on Data Distribution-Aware Test Selection for Deep Learning Enhancement
Q Hu, Y Guo, M Cordy, X Xie, L Ma, M Papadakis, Y Le Traon
TOSEM 2022, 2022
72022
GraphCode2Vec: Generic Code Embedding via Lexical and Program Dependence Analyses
W Ma, M Zhao, E Soremekun, Q Hu, J Zhang, M Papadakis, M Cordy, ...
MSR 2022, 2021
52021
Towards Exploring the Limitations of Active Learning: An Empirical Study
Q Hu, Y Guo, M Cordy, X Xie, W Ma, M Papadakis, Y Le Traon
ASE 2021, 2021
32021
Robust Active Learning: Sample-Efficient Training of Robust Deep Learning Models
Y Guo, Q Hu, M Cordy, M Papadakis, YL Traon
CAIN 2022, 2021
12021
MUTEN: Boosting Gradient-Based Adversarial Attacks via Mutant-Based Ensembles
Y Guo, Q Hu, M Cordy, M Papadakis, YL Traon
arXiv preprint arXiv:2109.12838, 2021
12021
Is Self-Attention Powerful to Learn Code Syntax and Semantics?
W Ma, M Zhao, X Xie, Q Hu, S Liu, J Zhang, W Wang, Y Liu
arXiv preprint arXiv:2212.10017, 2022
2022
DRE: density-based data selection with entropy for adversarial-robust deep learning models
Y Guo, Q Hu, M Cordy, M Papadakis, Y Le Traon
Neural Computing and Applications, 1-18, 2022
2022
Enhancing Mixup-Based Graph Learning for Language Processing via Hybrid Pooling
Z Dong, Q Hu, Y Guo, M Cordy, M Papadakis, YL Traon, J Zhao
arXiv preprint arXiv:2210.03123, 2022
2022
Enhancing Code Classification by Mixup-Based Data Augmentation
Z Dong, Q Hu, Y Guo, M Cordy, M Papadakis, YL Traon, J Zhao
SANER 2023, 2022
2022
Efficient Testing of Deep Neural Networks via Decision Boundary Analysis
Q Hu, Y Guo, X Xie, M Cordy, L Ma, M Papadakis, YL Traon
ICSE 2023, 2022
2022
CodeS: A Distribution Shift Benchmark Dataset for Source Code Learning
Q Hu, Y Guo, X Xie, M Cordy, L Ma, M Papadakis, YL Traon
ICSE 2023 NIER, 2022
2022
Characterizing and Understanding the Behavior of Quantized Models for Reliable Deployment
Q Hu, Y Guo, M Cordy, X Xie, W Ma, M Papadakis, YL Traon
arXiv preprint arXiv:2204.04220, 2022
2022
LaF: Labeling-Free Model Selection for Automated Deep Neural Network Reusing
Q Hu, Y Guo, M Cordy, X Xie, M Papadakis, YL Traon
arXiv preprint arXiv:2204.03994, 2022
2022
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Articles 1–18