180 citations · 951 across the 62 of their papers we have counts for
14 papers · 1 filter
Evaluating the Robustness of Test Selection Methods for Deep Neural Networks
Qiang Hu, Yuejun Guo, Xiaofei Xie +4
Testing deep learning-based systems is crucial but challenging due to the required time and labor for labeling collected raw data. To alleviate the labeling effort, multiple test s…
Neural Episodic Control with State Abstraction
Zhuo Li, Derui Zhu, Yujing Hu +6
Existing Deep Reinforcement Learning (DRL) algorithms suffer from sample inefficiency. Generally, episodic control-based approaches are solutions that leverage highly-rewarded past…
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…
NPC: Neuron Path Coverage via Characterizing Decision Logic of Deep Neural Networks
Xiaofei Xie, Tianlin Li, Jian Wang +4
Deep learning has recently been widely applied to many applications across different domains, e.g., image classification and audio recognition. However, the quality of Deep Neural…
Independent Reinforcement Learning for Weakly Cooperative Multiagent Traffic Control Problem
Chengwei Zhang, Shan Jin, Wanli Xue +3
The adaptive traffic signal control (ATSC) problem can be modeled as a multiagent cooperative game among urban intersections, where intersections cooperate to optimize their common…
Neuron Coverage-Guided Domain Generalization
Chris Xing Tian, Haoliang Li, Xiaofei Xie +2
This paper focuses on the domain generalization task where domain knowledge is unavailable, and even worse, only samples from a single domain can be utilized during training. Our m…
DeepRepair: Style-Guided Repairing for DNNs in the Real-world Operational Environment
Bing Yu, Hua Qi, Qing Guo +4
Deep neural networks (DNNs) are being widely applied for various real-world applications across domains due to their high performance (e.g., high accuracy on image classification).…
Retrieval-Augmented Generation for Code Summarization via Hybrid GNN
Shangqing Liu, Yu Chen, Xiaofei Xie +2
Source code summarization aims to generate natural language summaries from structured code snippets for better understanding code functionalities. However, automatic code summariza…
Stealing Deep Reinforcement Learning Models for Fun and Profit
Kangjie Chen, Shangwei Guo, Tianwei Zhang +2
This paper presents the first model extraction attack against Deep Reinforcement Learning (DRL), which enables an external adversary to precisely recover a black-box DRL model only…
Byzantine-resilient Decentralized Stochastic Gradient Descent
Shangwei Guo, Tianwei Zhang, Han Yu +4
Decentralized learning has gained great popularity to improve learning efficiency and preserve data privacy. Each computing node makes equal contribution to collaboratively learn a…
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…
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…