Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox
Picture for Jun Sun

Jun Sun

Probabilistic Verification of Neural Networks Against Group Fairness


Jul 18, 2021
Bing Sun, Jun Sun, Ting Dai, Lijun Zhang

Figure 1 for Probabilistic Verification of Neural Networks Against Group Fairness
Figure 2 for Probabilistic Verification of Neural Networks Against Group Fairness
Figure 3 for Probabilistic Verification of Neural Networks Against Group Fairness
Figure 4 for Probabilistic Verification of Neural Networks Against Group Fairness

  Access Paper or Ask Questions

Automatic Fairness Testing of Neural Classifiers through Adversarial Sampling


Jul 17, 2021
Peixin Zhang, Jingyi Wang, Jun Sun, Xinyu Wang, Guoliang Dong, Xingen Wang, Ting Dai, Jin Song Dong

Figure 1 for Automatic Fairness Testing of Neural Classifiers through Adversarial Sampling
Figure 2 for Automatic Fairness Testing of Neural Classifiers through Adversarial Sampling
Figure 3 for Automatic Fairness Testing of Neural Classifiers through Adversarial Sampling
Figure 4 for Automatic Fairness Testing of Neural Classifiers through Adversarial Sampling

  Access Paper or Ask Questions

Code Integrity Attestation for PLCs using Black Box Neural Network Predictions


Jun 15, 2021
Yuqi Chen, Christopher M. Poskitt, Jun Sun

Figure 1 for Code Integrity Attestation for PLCs using Black Box Neural Network Predictions
Figure 2 for Code Integrity Attestation for PLCs using Black Box Neural Network Predictions
Figure 3 for Code Integrity Attestation for PLCs using Black Box Neural Network Predictions
Figure 4 for Code Integrity Attestation for PLCs using Black Box Neural Network Predictions
* Accepted by the 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2021) 
  Access Paper or Ask Questions

Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems


May 22, 2021
Yifan Jia, Jingyi Wang, Christopher M. Poskitt, Sudipta Chattopadhyay, Jun Sun, Yuqi Chen

Figure 1 for Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems
Figure 2 for Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems
Figure 3 for Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems
Figure 4 for Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems
* Accepted by the International Journal of Critical Infrastructure Protection (IJCIP) 
  Access Paper or Ask Questions

Which to Match? Selecting Consistent GT-Proposal Assignment for Pedestrian Detection


Mar 18, 2021
Yan Luo, Chongyang Zhang, Muming Zhao, Hao Zhou, Jun Sun

Figure 1 for Which to Match? Selecting Consistent GT-Proposal Assignment for Pedestrian Detection
Figure 2 for Which to Match? Selecting Consistent GT-Proposal Assignment for Pedestrian Detection
Figure 3 for Which to Match? Selecting Consistent GT-Proposal Assignment for Pedestrian Detection
Figure 4 for Which to Match? Selecting Consistent GT-Proposal Assignment for Pedestrian Detection
* This manuscript is waiting for further improvement 
  Access Paper or Ask Questions

Attack as Defense: Characterizing Adversarial Examples using Robustness


Mar 13, 2021
Zhe Zhao, Guangke Chen, Jingyi Wang, Yiwei Yang, Fu Song, Jun Sun

Figure 1 for Attack as Defense: Characterizing Adversarial Examples using Robustness
Figure 2 for Attack as Defense: Characterizing Adversarial Examples using Robustness
Figure 3 for Attack as Defense: Characterizing Adversarial Examples using Robustness
Figure 4 for Attack as Defense: Characterizing Adversarial Examples using Robustness

  Access Paper or Ask Questions

Point Cloud Distortion Quantification based on Potential Energy for Human and Machine Perception


Mar 04, 2021
Qi Yang, Siheng Chen, Yiling Xu, Jun Sun, M. Salman Asif, Zhan Ma

Figure 1 for Point Cloud Distortion Quantification based on Potential Energy for Human and Machine Perception
Figure 2 for Point Cloud Distortion Quantification based on Potential Energy for Human and Machine Perception
Figure 3 for Point Cloud Distortion Quantification based on Potential Energy for Human and Machine Perception
Figure 4 for Point Cloud Distortion Quantification based on Potential Energy for Human and Machine Perception

  Access Paper or Ask Questions

CSTR: A Classification Perspective on Scene Text Recognition


Feb 22, 2021
Hongxiang Cai, Jun Sun, Yichao Xiong

Figure 1 for CSTR: A Classification Perspective on Scene Text Recognition
Figure 2 for CSTR: A Classification Perspective on Scene Text Recognition
Figure 3 for CSTR: A Classification Perspective on Scene Text Recognition
Figure 4 for CSTR: A Classification Perspective on Scene Text Recognition
* 10 pages, 4 figures 
  Access Paper or Ask Questions

Where, What, Whether: Multi-modal Learning Meets Pedestrian Detection


Dec 20, 2020
Yan Luo, Chongyang Zhang, Muming Zhao, Hao Zhou, Jun Sun

Figure 1 for Where, What, Whether: Multi-modal Learning Meets Pedestrian Detection
Figure 2 for Where, What, Whether: Multi-modal Learning Meets Pedestrian Detection
Figure 3 for Where, What, Whether: Multi-modal Learning Meets Pedestrian Detection
Figure 4 for Where, What, Whether: Multi-modal Learning Meets Pedestrian Detection
* This work is being revised. The updated version will be upload few months later 
  Access Paper or Ask Questions

Towards Repairing Neural Networks Correctly


Dec 03, 2020
Guoliang Dong, Jun Sun, Jingyi Wang, Xinyu Wang, Ting Dai

Figure 1 for Towards Repairing Neural Networks Correctly
Figure 2 for Towards Repairing Neural Networks Correctly
Figure 3 for Towards Repairing Neural Networks Correctly
Figure 4 for Towards Repairing Neural Networks Correctly

  Access Paper or Ask Questions

Improving Neural Network Verification through Spurious Region Guided Refinement


Oct 15, 2020
Pengfei Yang, Renjue Li, Jianlin Li, Cheng-Chao Huang, Jingyi Wang, Jun Sun, Bai Xue, Lijun Zhang

Figure 1 for Improving Neural Network Verification through Spurious Region Guided Refinement
Figure 2 for Improving Neural Network Verification through Spurious Region Guided Refinement
Figure 3 for Improving Neural Network Verification through Spurious Region Guided Refinement
Figure 4 for Improving Neural Network Verification through Spurious Region Guided Refinement

  Access Paper or Ask Questions

Holistic Combination of Structural and Textual Code Information for Context based API Recommendation


Oct 15, 2020
Chi Chen, Xin Peng, Zhenchang Xing, Jun Sun, Xin Wang, Yifan Zhao, Wenyun Zhao

Figure 1 for Holistic Combination of Structural and Textual Code Information for Context based API Recommendation
Figure 2 for Holistic Combination of Structural and Textual Code Information for Context based API Recommendation
Figure 3 for Holistic Combination of Structural and Textual Code Information for Context based API Recommendation
Figure 4 for Holistic Combination of Structural and Textual Code Information for Context based API Recommendation

  Access Paper or Ask Questions

SOCRATES: Towards a Unified Platform for Neural Network Verification


Jul 22, 2020
Long H. Pham, Jiaying Li, Jun Sun

Figure 1 for SOCRATES: Towards a Unified Platform for Neural Network Verification
Figure 2 for SOCRATES: Towards a Unified Platform for Neural Network Verification
Figure 3 for SOCRATES: Towards a Unified Platform for Neural Network Verification
Figure 4 for SOCRATES: Towards a Unified Platform for Neural Network Verification

  Access Paper or Ask Questions

Active Fuzzing for Testing and Securing Cyber-Physical Systems


May 28, 2020
Yuqi Chen, Bohan Xuan, Christopher M. Poskitt, Jun Sun, Fan Zhang

Figure 1 for Active Fuzzing for Testing and Securing Cyber-Physical Systems
Figure 2 for Active Fuzzing for Testing and Securing Cyber-Physical Systems
Figure 3 for Active Fuzzing for Testing and Securing Cyber-Physical Systems
Figure 4 for Active Fuzzing for Testing and Securing Cyber-Physical Systems
* Accepted by the ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2020) 
  Access Paper or Ask Questions

Learning efficient structured dictionary for image classification


Feb 09, 2020
Zi-Qi Li, Jun Sun, Xiao-Jun Wu, He-Feng Yin

Figure 1 for Learning efficient structured dictionary for image classification
Figure 2 for Learning efficient structured dictionary for image classification
Figure 3 for Learning efficient structured dictionary for image classification
Figure 4 for Learning efficient structured dictionary for image classification
* Submitted to Journal of Electronic Imaging 
  Access Paper or Ask Questions

Multiplication fusion of sparse and collaborative-competitive representation for image classification


Jan 20, 2020
Zi-Qi Li, Jun Sun, Xiao-Jun Wu, He-Feng Yin

Figure 1 for Multiplication fusion of sparse and collaborative-competitive representation for image classification
Figure 2 for Multiplication fusion of sparse and collaborative-competitive representation for image classification
Figure 3 for Multiplication fusion of sparse and collaborative-competitive representation for image classification
Figure 4 for Multiplication fusion of sparse and collaborative-competitive representation for image classification
* submitted to International Journal of Machine Learning and Cybernetics 
  Access Paper or Ask Questions

There is Limited Correlation between Coverage and Robustness for Deep Neural Networks


Nov 14, 2019
Yizhen Dong, Peixin Zhang, Jingyi Wang, Shuang Liu, Jun Sun, Jianye Hao, Xinyu Wang, Li Wang, Jin Song Dong, Dai Ting

Figure 1 for There is Limited Correlation between Coverage and Robustness for Deep Neural Networks
Figure 2 for There is Limited Correlation between Coverage and Robustness for Deep Neural Networks
Figure 3 for There is Limited Correlation between Coverage and Robustness for Deep Neural Networks
Figure 4 for There is Limited Correlation between Coverage and Robustness for Deep Neural Networks

  Access Paper or Ask Questions

Finite-Sample Analysis of Decentralized Temporal-Difference Learning with Linear Function Approximation


Nov 03, 2019
Jun Sun, Gang Wang, Georgios B. Giannakis, Qinmin Yang, Zaiyue Yang

Figure 1 for Finite-Sample Analysis of Decentralized Temporal-Difference Learning with Linear Function Approximation
* 25 pages, 3 figures 
  Access Paper or Ask Questions

Understanding Social Networks using Transfer Learning


Oct 16, 2019
Jun Sun, Steffen Staab, Jérôme Kunegis

Figure 1 for Understanding Social Networks using Transfer Learning
Figure 2 for Understanding Social Networks using Transfer Learning
Figure 3 for Understanding Social Networks using Transfer Learning
Figure 4 for Understanding Social Networks using Transfer Learning
* IEEE Computer (Volume: 51, Issue: 6, June 2018) 
 * 11 pages, 4 figures, IEEE Computer. arXiv admin note: text overlap with arXiv:1611.02941 
  Access Paper or Ask Questions

A Dual Camera System for High Spatiotemporal Resolution Video Acquisition


Sep 28, 2019
Ming Cheng, Zhan Ma, M. Salman Asif, Yiling Xu, Haojie Liu, Wenbo Bao, Jun Sun

Figure 1 for A Dual Camera System for High Spatiotemporal Resolution Video Acquisition
Figure 2 for A Dual Camera System for High Spatiotemporal Resolution Video Acquisition
Figure 3 for A Dual Camera System for High Spatiotemporal Resolution Video Acquisition
Figure 4 for A Dual Camera System for High Spatiotemporal Resolution Video Acquisition

  Access Paper or Ask Questions

Analyzing Recurrent Neural Network by Probabilistic Abstraction


Sep 22, 2019
Guoliang Dong, Jingyi Wang, Jun Sun, Yang Zhang, Xinyu Wang, Ting Dai, Jin Song Dong

Figure 1 for Analyzing Recurrent Neural Network by Probabilistic Abstraction
Figure 2 for Analyzing Recurrent Neural Network by Probabilistic Abstraction
Figure 3 for Analyzing Recurrent Neural Network by Probabilistic Abstraction
Figure 4 for Analyzing Recurrent Neural Network by Probabilistic Abstraction

  Access Paper or Ask Questions

Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients


Sep 17, 2019
Jun Sun, Tianyi Chen, Georgios B. Giannakis, Zaiyue Yang

Figure 1 for Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients
Figure 2 for Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients
Figure 3 for Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients
Figure 4 for Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients
* Accepted in NeurIPS 2019 
  Access Paper or Ask Questions

Learning from Adversarial Features for Few-Shot Classification


Mar 25, 2019
Wei Shen, Ziqiang Shi, Jun Sun

Figure 1 for Learning from Adversarial Features for Few-Shot Classification
Figure 2 for Learning from Adversarial Features for Few-Shot Classification
Figure 3 for Learning from Adversarial Features for Few-Shot Classification
Figure 4 for Learning from Adversarial Features for Few-Shot Classification

  Access Paper or Ask Questions

Safeguarded Dynamic Label Regression for Generalized Noisy Supervision


Mar 06, 2019
Jiangchao Yao, Ya Zhang, Ivor W. Tsang, Jun Sun

Figure 1 for Safeguarded Dynamic Label Regression for Generalized Noisy Supervision
Figure 2 for Safeguarded Dynamic Label Regression for Generalized Noisy Supervision
Figure 3 for Safeguarded Dynamic Label Regression for Generalized Noisy Supervision
Figure 4 for Safeguarded Dynamic Label Regression for Generalized Noisy Supervision
* Submitted to Transactions on Image Processing 
  Access Paper or Ask Questions

Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing


Jan 18, 2019
Jingyi Wang, Guoliang Dong, Jun Sun, Xinyu Wang, Peixin Zhang

Figure 1 for Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing
Figure 2 for Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing
Figure 3 for Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing
Figure 4 for Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing
* Accepted by ICSE 2019 
  Access Paper or Ask Questions

Learning from Mutants: Using Code Mutation to Learn and Monitor Invariants of a Cyber-Physical System


Jun 13, 2018
Yuqi Chen, Christopher M. Poskitt, Jun Sun

Figure 1 for Learning from Mutants: Using Code Mutation to Learn and Monitor Invariants of a Cyber-Physical System
Figure 2 for Learning from Mutants: Using Code Mutation to Learn and Monitor Invariants of a Cyber-Physical System
Figure 3 for Learning from Mutants: Using Code Mutation to Learn and Monitor Invariants of a Cyber-Physical System
Figure 4 for Learning from Mutants: Using Code Mutation to Learn and Monitor Invariants of a Cyber-Physical System
* In Proc. IEEE Symposium on Security & Privacy (S&P 2018), pages 648-660. IEEE, 2018 
 * Accepted by IEEE S&P 2018 
  Access Paper or Ask Questions

Detecting Adversarial Samples for Deep Neural Networks through Mutation Testing


May 17, 2018
Jingyi Wang, Jun Sun, Peixin Zhang, Xinyu Wang

Figure 1 for Detecting Adversarial Samples for Deep Neural Networks through Mutation Testing
Figure 2 for Detecting Adversarial Samples for Deep Neural Networks through Mutation Testing
Figure 3 for Detecting Adversarial Samples for Deep Neural Networks through Mutation Testing
Figure 4 for Detecting Adversarial Samples for Deep Neural Networks through Mutation Testing
* Sumitted to NIPS 2018 
  Access Paper or Ask Questions

Toward `verifying' a Water Treatment System


May 10, 2018
Jingyi Wang, Jun Sun, Yifan Jia, Shengchao Qin, Zhiwu Xu

Figure 1 for Toward `verifying' a Water Treatment System
Figure 2 for Toward `verifying' a Water Treatment System
Figure 3 for Toward `verifying' a Water Treatment System
Figure 4 for Toward `verifying' a Water Treatment System
* Accepted by FM 2018 
  Access Paper or Ask Questions

Deep Learning from Noisy Image Labels with Quality Embedding


Nov 02, 2017
Jiangchao Yao, Jiajie Wang, Ivor Tsang, Ya Zhang, Jun Sun, Chengqi Zhang, Rui Zhang

Figure 1 for Deep Learning from Noisy Image Labels with Quality Embedding
Figure 2 for Deep Learning from Noisy Image Labels with Quality Embedding
Figure 3 for Deep Learning from Noisy Image Labels with Quality Embedding
Figure 4 for Deep Learning from Noisy Image Labels with Quality Embedding
* Under review for Transactions on Image Processing 
  Access Paper or Ask Questions

Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning


Sep 25, 2017
Jun Inoue, Yoriyuki Yamagata, Yuqi Chen, Christopher M. Poskitt, Jun Sun

Figure 1 for Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning
Figure 2 for Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning
Figure 3 for Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning
Figure 4 for Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning
* Proc. IEEE International Conference on Data Mining Workshops (ICDMW 2017): Data Mining for Cyberphysical and Industrial Systems (DMCIS 2017), pages 1058-1065. IEEE, 2017 
  Access Paper or Ask Questions