Yuhang Liu

Dr Yuhang Liu

Postdoc

Australian Institute for Machine Learning - Projects

Faculty of Sciences, Engineering and Technology

My current major research topics are about:

  1. building the bridge between causality and machine learning, e.g., causal representation learning, multi domain/modal Learning;

  2. building the bridge between Bayesian learning and deep learning, e.g., Bayesian deep learning and deep Bayesian learning;

  3. inverse problems in various applications, e.g., computer vision, signal processing.

Homepage: https://sites.google.com/view/yuhangliu/homepage

  • Journals
    Year Citation
    2024 Yan, Q., Wang, H., Ma, Y., Liu, Y., Dong, W., Woźniak, M., & Zhang, Y. (2024). Uncertainty estimation in HDR imaging with Bayesian neural networks. Pattern Recognition, 156, 110802.
    DOI
    2020 Wen, S., Deng, L., & Liu, Y. (2020). Distributed optimization via primal and dual decompositions for delay-constrained FANETs. Ad Hoc Networks, 109, 1-14.
    DOI Scopus11
    2018 Liu, Y., Dong, W., & Zhou, M. (2018). Frame-Based Variational Bayesian Learning for Independent or Dependent Source Separation. IEEE Transactions on Neural Networks and Learning Systems, 29(10), 4983-4996.
    DOI Scopus11
    2016 Dong, W. Y., Kang, L. L., Liu, Y. H., & Li, K. S. (2016). Opposition-based particle swarm optimization with adaptive elite mutation and nonlinear inertia weight. Tongxin Xuebao/Journal on Communications, 37(12), 1-10.
    DOI Scopus19

  • Conference Papers
    Year Citation
    2024 Liu, Y., Zhang, Z., Gong, D., Gong, M., Huang, B., van den Hengel, A., . . . Shi, J. Q. (2024). IDENTIFIABLE LATENT POLYNOMIAL CAUSAL MODELS THROUGH THE LENS OF CHANGE. In 12th International Conference on Learning Representations, ICLR 2024. Online: ICLR.
    2022 Yan, Q., Zhang, S., Chen, W., Liu, Y., Zhang, Z., Zhang, Y., . . . Gong, D. (2022). A Lightweight Network for High Dynamic Range Imaging. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Vol. 2022-June (pp. 823-831). Online: IEEE.
    DOI Scopus9 WoS1
    2022 Perez-Pellitero, E., Catley-Chandar, S., Shaw, R., Leonardis, A., Timofte, R., Zhang, Z., . . . Park, C. Y. (2022). NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Vol. 2022-June (pp. 1008-1022). Online: IEEE.
    DOI Scopus27 WoS7
    2022 Yan, Q., Gong, D., Liu, Y., Van Den Hengel, A., & Shi, J. Q. (2022). Learning Bayesian Sparse Networks with Full Experience Replay for Continual Learning. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2022-June (pp. 109-118). Online: IEEE.
    DOI Scopus26 WoS5
    2022 Zhang, Z., Ng, I., Gong, D., Liu, Y., Abbasnejad, E. M., Gong, M., . . . Shi, J. Q. (2022). Truncated Matrix Power Iteration for Differentiable DAG Learning. In Advances in Neural Information Processing Systems Vol. 35 (pp. 13 pages). Online: Neural information processing systems foundation.
    Scopus9
    2020 Yang, L., Liu, Y., & Fan, W. (2020). Axial Data Modeling with Collapsed Nonparametric Watson Mixture Models and Its Application to Depth Image Analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12306 LNCS (pp. 17-28). Switzerland: Springer International Publishing.
    DOI Scopus1
    2019 Liu, Y., Dong, W., Song, W., & Zhang, L. (2019). Bayesian nonnegative matrix factorization with a truncated spike-and-slab prior. In Proceedings - IEEE International Conference on Multimedia and Expo Vol. 2019-July (pp. 1450-1455). online: IEEE.
    DOI Scopus1
    2019 Liu, Y., Dong, W., Zhang, L., Gong, D., & Shi, Q. (2019). Variational bayesian dropout with a hierarchical prior. In Proceedings: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Vol. 2019-June (pp. 7117-7126). online: IEEE.
    DOI Scopus15 WoS10
    2018 Liu, Y., Dong, W., Gong, D., Zhang, L., & Shi, Q. (2018). Deblurring natural image using super-gaussian fields. In Proceedings of the 15th European Conference on Computer Vision as published in Lecture Notes in Computer Science Vol. 11205 LNCS (pp. 467-484). Switzerland: Springer Nature.
    DOI Scopus8 WoS17
  • Position: Postdoc
  • Email: yuhang.liu01@adelaide.edu.au
  • Campus: North Terrace
  • Building: Australian Institute for Machine Learning
  • Org Unit: Australian Institute for Machine Learning - Operations
Connect With Me
External Profiles