Publication

Jump to: Conference Papers Journal Papers Book

Conference Papers

  1. Yihe Wang*, Yu Han*, Haishuai Wang, and Xiang Zhang. Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series. (NeurIPS 2023), New Orleans, USA, December 10-16, 2023.
  2. Gaotang Li, Marlena Duda, Xiang Zhang, Danai, Koutra and Yujun Yan. Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks. (KDD 2023), Long Beach, USA, August 6 - August 10, 2023.
  3. Yihe Wang, Mohammad Khalili and Xiang Zhang. Towards Fair Representation Learning in Knowledge Graph with Stable Adversarial Debiasing.. 2022 IEEE International Conference on Data Mining Workshops (ICDMW).
  4. Xiang Zhang, Ziyuan Zhao, Theodoros Tsiligkaridis, and Marinka Zitnik. Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency. (NeurIPS 2022), New Orleans, USA, November 28 - December 3, 2022.
  5. Xiang Zhang, Marko Zeman, Theodoros Tsiligkaridis, and Marinka Zitnik. Graph-Guided Network for Irregularly Sampled Multivariate Time Series. (ICLR 2022), Virtual, April 25 - 29, 2022.

  6. Lei Wang, Xiang Zhang, Yuanshuang Jiang, Yong Zhang, Chenren Xu, Ruiyang Gao, and Daqing Zhang. Watching Your Phone’s Back: Gesture Recognition by Sensing Acoustical Structure-borne Propagation. (UbiComp 2021), 21-26th September, 2021.

  7. Xiang Zhang and Mainka Zitnik. GNNGuard: Defending Graph Neural Networks against Adversarial Attacks (NeurIPS 2020), Vancouver Convention Center, Vancouver Canada, December 6 - 12, 2020.

  8. Xiang Zhang, Lina Yao, Xianzhi Wang, Wenjie Zhang, Shuai Zhang, and Yunhao Liu. Know Your Mind:Adaptive Cognitive Activity Recognition with Reinforced Attentive Convolutional Neurual Networks. The 2019 IEEE International Conference on Data Mining (ICDM 2019). Beijing, China, November 8-11, 2019.

  9. Xiaocong Chen, Chaoran Huang, Xiang Zhang, Wei Liu, and Lina Yao. Distributed Expert Representation Learning in Question Answering Community. The 15th Advanced Data Mining and Applications (ADMA 2019). Dalian, China, November 21-23, 2019.


  10. Xiang Zhang, Xiaocong Chen, Lina Yao, Chang Ge, and Manqing Dong. Deep Neural Network Hyperparameter Optimization with Orthogonal Array Tuning. The 26th Interna tional Conference On Neural Information Processing (ICONIP 2019). Sydney, Australia, December 12-15, 2019.

  11. Xiang Zhang, Xiaocong Chen, Manqing Dong, Huan Liu, Chang Ge, and Lina Yao. Multi-task Generative Adversarial Learning on Geometrical Shape Reconstruction from EEG Brain Signals. The 26th International Conference On Neural Information Processing (ICONIP 2019). Sydney, Australia, December 12-15, 2019.

  12. Xiang Zhang, Lina Yao, and Feng Yuan. Adversarial Variational Embedding for Robust Semi-supervised Learning. The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019). Research Track. Anchorage, Alaska, August 4 - 8, 2019.

  13. Manqing Dong, Lina Yao, Xianzhi Wang, Boualem Benatallah, Xiang Zhang, and Quan Z. Sheng. Dual-stream Self-Attentive Random Forest for False Information Detection. International Joint Conference on Neural Networks (IJCNN 2019), Budapest, Hungary, July 14 - 19, 2019.

  14. Xiang Zhang, Lina Yao, Chaoran Huang, Sen Wang, Mingkui Tan, Guodong Long, and Can Wang. Multi-modality Sensor Data Classification with Selective Attention. The 27th International Joint Conference on Artificial Intelligence (IJCAI 2018), tockholm, Sweden, July 13-19, 2018.

  15. Weitong Chen, Sen Wang, Xiang Zhang, Lina Yao, Lin Yue, Buyue Qian, and Xue Li. EEG-based Motion Intention Recognition via Multi-task RNNs. SIAM International Conference on Data Mining (SDM 2018), San Diego, USA, May 3 - 5, 2018.

  16. Xiang Zhang. Context-aware Human Intent Inference for Improving Human Machine Cooperation. IEEE International Conference on Pervasive Computing and Communications Ph.D. Forum (PerCom 2018). Athens, Greece, March 19-23, 2018.

  17. Xiaodong Ning, Lina Yao, Xianzhi Wang, Boualem Benatallah, Shuai Zhang, and Xiang Zhang. Data-Augmented Regression with Generative Convolutional Network. The 19th International Conference on Web Information Systems Engineering WISE(2018), Dubai, United Arab Emirates, November 12 - 15, 2018.

  18. Xiang Zhang, Lina Yao, Salil S. Kanhere, Yunhao Liu, Tao Gu, and Kaixuan Chen, MindID: Person Identification from Brain Waves through Attention-based Recurrent Neural Network. 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing (Ubicomp 2018). Singapore, October 8-12, 2018.

  19. Dalin Zhang, Lina Yao, Xiang Zhang, Sen Wang, Weitong Chen, and Robert Boots. EEG-based Intention Recognition from Spatio-Temporal Representations via Cascade and Parallel Convolutional Recurrent Neural Networks. AAAI 2018, Hilton New Orleans Riverside, New Orleans, USA, February 2-7, 2018.

  20. Xiang Zhang, Lina Yao, Quan Z. Sheng, Salil S. Kanhere, Tao Gu, and Dalin Zhang, Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals. IEEE International Conference on Pervasive Computing and Communications (PerCom 2018). Athens, Greece, March 19-23, 2018.

  21. Xiang Zhang, Lina Yao, Chaoran Huang, Quan Z. Sheng, and Xianzhi Wang, Intent Recognition in Smart Living Through Deep Recurrent Neural Networks. The 24th International Conference On Neural Information Processing (ICONIP 2017). Guangzhou, China, November 14-18, 2017.

  22. Xiang Zhang, Lina Yao, Dalin Zhang, Xianzhi Wang, Quan Z. Sheng, and Tao Gu. Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis. The 14th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (Mobiquitous 2017). Melbourne, Australia Nov 7 - 10, 2017.




Journal Papers

  1. Biao Ye, Changyu Yin, Xiang Zhang, and Rui Yin. MTLNFM: Multi-task Learning to Predict Patient Clinical Outcomes with Neural Factorization Machine. AMIA 2023 Annual Symposium.
  2. Ziyu Liu, Azadeh Alavi, Minyi Li, and Xiang Zhang. Self-Supervised Contrastive Learning for Medical Time Series: A Systematic Review. Sensors (2023).
  3. Xiang Zhang, Marissa Sumathipala, and Marinka Zitnik. "Population-scale patient safety data reveal inequalities in adverse events before and during COVID-19 pandemic." Nature Computational Science (2021).

  4. Lei Bai, Lina Yao, Xianzhi Wang, Can Li, and Xiang Zhang. Deep Spatial-Temporal Sequence Modeling for Multi-Step Passenger Demand Prediction. Future Generation Computer Systems (FGCS) (2020).

  5. Weitao Xu, Xiang Zhang, Chengwen Luo, Lina Yao, Jiangqiang Li, Zhong Ming, and Bo Wei. A Multi-view CNN-based Acoustic Classification System for Automatic Animal Specie Identification. Ad Hoc Networks.

  6. Xiang Zhang, Lina Yao, Manqing Dong, Zhe Liu, Yu Zhang, and Yong Li. Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection. IEEE Journal of Biomedical and Health Informatics (J-BHI).

  7. Xiang Zhang, Lina Yao, Xianzhi Wang, Jessica Monaghan, David Mcalpine, and Yu Zhang. A Survey on Deep Learning-based Non-Invasive Brain Signals:Recent Advances and New Frontiers. Journal of Neural Engineering.

  8. Xiang Zhang, Lina Yao, Chaoran Huang, Tao Gu, Zheng Yang, and Yunhao Liu. DeepKey: A Multimodal Biometric Authentication System via Deep Decoding Gaits and Brainwaves. ACM Transaction on Intelligent Systems and Technology (TIST).

  9. Shuai Zhang, Lina Yao, Bin Wu, Xiwei Xu, Xiang Zhang, and Liming Zhu. Unraveling Metric Vector Spaces with Factorization for Recommendation. IEEE Transactions on Industrial Informatics (TII).

  10. Xiang Zhang, Lina Yao, Shuai Zhang, Salil S. Kanhere, Quan Z. Sheng, and Yunhao Liu. Internet of Things Meets Brain-Computer Interface: A Unified Deep Learning Framework for Enabling Human-Thing Cognitive Interactivity. IEEE Internet of Things Journal.


  11. Chaoran Huang, Lina Yao, Xianzhi Wang, Boualem Benatallah, and Xiang Zhang. Software Expert Discovery via Knowledge Domain Embeddings in a Collaborative Network. Pattern Recognition Letter (PRL).

  12. Xiang Zhang, Computer Science & Engineering, Faculty of Engineering, UNSW, 2020. Data-Efficient Deep Representation Learning for Brain-Computer Interface and Its Applications. (Ph.D. Thesis)


Book

  1. Xiang Zhang and Lina Yao, Deep Learning for Brain Computer Interface: Representations, Algorithms and Applications. World Scientific Publishing Europe Ltd, United Kingdom.