Guansong Pang

Guansong Pang

Australian Institute for Machine Learning - Projects

Faculty of Engineering, Computer and Mathematical Sciences

Eligible to supervise Masters and PhD - email supervisor to discuss availability.


I am a research fellow with the Australian Institute for Machine Learning at The University of Adelaide. My current research interests include anomaly detection, deep learning, weakly supervised learning, reinforcement learning, non-IID learning, fake news detection, and their applications in cybersecurity, Fintech, and healthcare.

My research interests are generally on machine learning and their applications, with a recent focus on deep learning, anomaly detection, reinforcement learning, person reidentification, and fake news detection.

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  • Journals

    Year Citation
    2021 Tian, Y., Pang, G., Chen, Y., Singh, R., Verjans, J. W., & Carneiro, G. (2021). Weakly-supervised Video Anomaly Detection with Contrastive Learning of Long and Short-range Temporal Features.. CoRR, abs/2101.10030.
    2021 Tian, Y., Pang, G., Liu, F., Chen, Y., Shin, S. -H., Verjans, J. W., . . . Carneiro, G. (2021). Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images.. CoRR, abs/2103.03423.
    2021 Zhang, J., Xie, Y., Pang, G., Liao, Z., Verjans, J., Li, W., . . . Xia, Y. (2021). Viral Pneumonia Screening on Chest X-Rays Using Confidence-Aware Anomaly Detection.. IEEE Trans. Medical Imaging, 40, 879-890.
    2020 Pang, G., & Cao, L. (2020). Heterogeneous univariate outlier ensembles in multidimensional data. ACM Transactions on Knowledge Discovery from Data, 14(6), 68-1-68-27.
    DOI
    2020 Zhang, J., Xie, Y., Pang, G., Liao, Z., Verjans, J., Li, W., . . . Xia, Y. (2020). Viral pneumonia screening on chest X-rays using confidence-aware anomaly detection. IEEE Transactions on Medical Imaging, 40(3), 1-12.
    DOI Europe PMC7
    2020 Zheng, D., Pang, G., Liu, B., Chen, L., & Yang, J. (2020). Learning transferable deep convolutional neural networks for the classification of bacterial virulence factors.. Bioinformatics, 36(12), 3693-3702.
    DOI Scopus1 WoS1
    2019 Jian, S., Pang, G., Cao, L., Lu, K., & Gao, H. (2019). CURE: Flexible Categorical Data Representation by Hierarchical Coupling Learning. IEEE Trans. Knowl. Data Eng., 31(5), 853-866.
    DOI
    2018 Pang, G., Cao, L., Chen, L., & Liu, H. (2018). Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection. CoRR, abs/1806.04808.
    2016 Pang, G., Ting, K. M., Albrecht, D. W., & Jin, H. (2016). ZERO++: Harnessing the Power of Zero Appearances to Detect Anomalies in Large-Scale Data Sets. The Journal of Artificial Intelligence Research, 57, 593-620.
    2015 Pang, G., Jin, H., & Jiang, S. (2015). CenKNN: a scalable and effective text classifier. Data Mining and Knowledge Discovery, 29(3), 593-625.
    DOI
    2013 Pang, G., & Jiang, S. (2013). A generalized cluster centroid based classifier for text categorization. Information Processing & Management, 49(2), 576-586.
    DOI
    2012 Jiang, S., Pang, G., Wu, M., & Kuang, L. (2012). An improved K-nearest-neighbor algorithm for text categorization. Expert Systems with Applications, 39(1), 1503-1509.
    DOI
    Pang, G., Pham, N. T. A., Baker, E., Bentley, R., & Hengel, A. V. D. (n.d.). Deep Multi-task Learning for Depression Detection and Prediction in
    Longitudinal Data.
  • Conference Papers

    Year Citation
    2020 Pang, G., Yan, C., Shen, C., van den Hengel, A., & Bai, X. (2020). Self-trained deep ordinal regression for end-to-end video anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020) (pp. 12170-12179). online: IEEE.
    DOI Scopus1
    2020 Wang, H., Pang, G., Shen, C., & Ma, C. (2020). Unsupervised representation learning by predicting random distances. In IJCAI International Joint Conference on Artificial Intelligence Vol. 2021-January (pp. 2950-2956). online: AAAI Press.
    Scopus2
    2020 Zhao, J., Yang, Y., Pang, G., Lv, L., Shang, H., Sun, Z., & Yang, W. (2020). Learning Discriminative Neural Sentiment Units for Semi-supervised Target-Level Sentiment Classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12085 LNAI (pp. 798-810). Switzerland: Springer.
    DOI Scopus1
    2019 Pang, G., Shen, C., & Van Den Hengel, A. (2019). Deep anomaly detection with deviation networks. In KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 353-362). New York: Association of Computing Machinery.
    DOI Scopus14 WoS5
    2019 Yan, C., Pang, G., Bai, X., Shen, C., Zhou, J., & Hancock, E. (2019). Deep hashing by discriminating hard examples. In MM 2019 - Proceedings of the 27th ACM International Conference on Multimedia (pp. 1535-1542). online: ACM.
    DOI Scopus2 WoS1
    2018 Pang, G., Cao, L., Chen, L., Lian, D., & Liu, H. (2018). Sparse Modeling-Based Sequential Ensemble Learning for Effective Outlier Detection in High-Dimensional Numeric Data. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018 (pp. 3892-3899). online: AAAI.
    2018 Pang, G., Cao, L., Chen, L., & Liu, H. (2018). Learning representations of ultrahigh-dimensional data for random distance-based outlier detection. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2041-2050). New York: Association for Computing Machinery.
    DOI
    2017 Pang, G., Xu, H., Cao, L., & Zhao, W. (2017). Selective Value Coupling Learning for Detecting Outliers in High-Dimensional Categorical Data. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM 2017, Singapore, November 06 - 10, 2017 (pp. 807-816). online: ACM.
    DOI
    2017 Jian, S., Cao, L., Pang, G., Lu, K., & Gao, H. (2017). Embedding-based Representation of Categorical Data by Hierarchical Value Coupling Learning. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19-25, 2017 (pp. 1937-1943). online: IJCAI.
    DOI
    2017 Pang, G., Cao, L., Chen, L., & Liu, H. (2017). Learning Homophily Couplings from Non-IID Data for Joint Feature Selection and Noise-Resilient Outlier Detection. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19-25, 2017 (pp. 2585-2591). online: IJCAI.
    DOI
    2016 Pang, G., Cao, L., Chen, L., & Liu, H. (2016). Unsupervised Feature Selection for Outlier Detection by Modelling Hierarchical Value-Feature Couplings. In IEEE 16th International Conference on Data Mining, ICDM 2016, December 12-15, 2016, Barcelona, Spain (pp. 410-419). Online: IEEE.
    DOI
    2016 Pang, G., Cao, L., & Chen, L. (2016). Outlier Detection in Complex Categorical Data by Modeling the Feature Value Couplings. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016 (pp. 1902-1908). Online: AAAI Press / International Joint Conferences on Artificial Intelligence.
    2015 Pang, G., Ting, K. M., & Albrecht, D. W. (2015). LeSiNN: Detecting Anomalies by Identifying Least Similar Nearest Neighbours. In Proceedings of the IEEE International Conference on Data Mining Workshop, ICDMW 2015 (pp. 623-630). Online: IEEE.
    DOI
    2013 Pang, G., Jiang, S., & Chen, D. (2013). A Simple Integration of Social Relationship and Text Data for Identifying Potential Customers in Microblogging. In Unknown Conference (pp. 397-409). Springer Berlin Heidelberg.
    DOI
    2013 Pang, G., Jin, H., & Jiang, S. (2013). An effective class-centroid-based dimension reduction method for text classification. In Proceedings of the 22nd International Conference on World Wide Web - WWW '13 Companion. ACM Press.
    DOI
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  • Current Higher Degree by Research Supervision (University of Adelaide)

    Date Role Research Topic Program Degree Type Student Load Student Name
    2020 Co-Supervisor Weakly Supervised Semantic Segmentation Doctor of Philosophy Doctorate Full Time Mr Choubo Ding
    2020 Co-Supervisor Text Visual Question Answering Doctor of Philosophy Doctorate Full Time Mr Xinyu Wang
    2019 Co-Supervisor Semantic 3D Scene Reconstruction Doctor of Philosophy Doctorate Full Time Mr Libo Sun
  • Past Higher Degree by Research Supervision (University of Adelaide)

    Date Role Research Topic Program Degree Type Student Load Student Name
    2019 - 2021 Co-Supervisor Efficient Fully-Convolutional Networks for Image Perception Doctor of Philosophy Doctorate Full Time Mr Hao Chen
  • Email: guansong.pang@adelaide.edu.au
  • Campus: North Terrace
  • Building: Australian Institute for Machine Learning, floor G
  • Room: G.29.B
  • Org Unit: The University of Adelaide

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