10 citations · 32 across the 14 of their papers we have counts for
6 papers · 1 filter
On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language Processing
Zeming Dong, Qiang Hu, Zhenya Zhang +5
Graph neural network (GNN)-based graph learning has been popular in natural language and programming language processing, particularly in text and source code classification. Typic…
MIXCODE: Enhancing Code Classification by Mixup-Based Data Augmentation
Zeming Dong, Qiang Hu, Yuejun Guo +5
Inspired by the great success of Deep Neural Networks (DNNs) in natural language processing (NLP), DNNs have been increasingly applied in source code analysis and attracted signifi…
Aries: Efficient Testing of Deep Neural Networks via Labeling-Free Accuracy Estimation
Qiang Hu, Yuejun Guo, Xiaofei Xie +4
Deep learning (DL) plays a more and more important role in our daily life due to its competitive performance in industrial application domains. As the core of DL-enabled systems, d…
CodeS: Towards Code Model Generalization Under Distribution Shift
Qiang Hu, Yuejun Guo, Xiaofei Xie +4
Distribution shift has been a longstanding challenge for the reliable deployment of deep learning (DL) models due to unexpected accuracy degradation. Although DL has been becoming…
Characterizing and Understanding the Behavior of Quantized Models for Reliable Deployment
Qiang Hu, Yuejun Guo, Maxime Cordy +4
Deep Neural Networks (DNNs) have gained considerable attention in the past decades due to their astounding performance in different applications, such as natural language modeling,…
LaF: Labeling-Free Model Selection for Automated Deep Neural Network Reusing
Qiang Hu, Yuejun Guo, Maxime Cordy +3
Applying deep learning to science is a new trend in recent years which leads DL engineering to become an important problem. Although training data preparation, model architecture d…