Dr Ziquan Liu
Lecturer
School of Electronic Engineering and Computer Science
Queen Mary University of London
Queen Mary University of London
Research
Reliable Machine Learning, Uncertainty Quantification, Computer Vision
Interests
My research interest is machine learning, including trustworthy and robust machine learning, uncertainty of foundation models and interpretable machine learning. The main goal of my research is to quantify and mitigate the risk of machine learning systems so that the deployment of machine learning benefits each individual in our society.Publications
Publications of specific relevance to the Centre for Multimodal AI2024
Chen F, Lin W, Liu Z and Chan AB (2024). A Secure Image Watermarking Framework with Statistical Guarantees via Adversarial Attacks on Secret Key Networks. 10-11-2024
Liu Z, Cui Y, Yan Y, Xu Y, Ji X, Liu X and Chan AB (2024). The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks. 01-01-2024
2023
Cui Y, Mao Y, Liu Z, Li Q, Chan AB, Liu X, Kuo T-W and Xue CJ (2023). Variational Nested Dropout. IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers (IEEE) vol. 45 (8), 10519-10534. 30-06-2023
Wu Q, Yang T, Liu Z, Wu B, Shan Y and Chan AB (2023). DropMAE: Masked Autoencoders with Spatial-Attention Dropout for Tracking Tasks. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 24-06-2023
Lan H, Liu Z, Hsiao JH, Yu D and Chan AB (2023). Clustering Hidden Markov Models With Variational Bayesian Hierarchical EM. IEEE Transactions on Neural Networks and Learning Systems, Institute of Electrical and Electronics Engineers (IEEE) vol. 34 (3), 1537-1551. 28-02-2023
Cui Y, Liu Z, Chen Y, Lu Y, Yu X, Liu X, Kuo TW, Rodrigues MRD, Xue CJ and Chan AB (2023). Retrieval-Augmented Multiple Instance Learning. 01-01-2023
Cui Y, Liu Z, Liu X, Wang C, Kuo TW, Xue CJ and Chan AB (2023). BAYES-MIL: A NEW PROBABILISTIC PERSPECTIVE ON ATTENTION-BASED MULTIPLE INSTANCE LEARNING FOR WHOLE SLIDE IMAGES. 01-01-2023
2022
Liu Z, Yu L, Hsiao JH and Chan AB (2022). PRIMAL-GMM: PaRametrIc MAnifold Learning of Gaussian Mixture Models. IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers (IEEE) vol. 44 (6), 3197-3211. 05-05-2022
Liu Z, Xu Y, Qian Q, Li H, Ji X, Chan AB and Jin R (2022). Improved Fine-Tuning by Better Leveraging Pre-Training Data. 01-01-2022
Liu Z and Chan AB (2022). Boosting Adversarial Robustness From The Perspective of Effective Margin Regularization. 01-01-2022
2021
Cui Y, Liu Z, Li Q, Chan AB and Xue CJ (2021). Bayesian Nested Neural Networks for Uncertainty Calibration and Adaptive Compression. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 25-06-2021
Wan J, Liu Z and Chan AB (2021). A Generalized Loss Function for Crowd Counting and Localization. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 25-06-2021
2020
Cui Y, Liu Z, Yao W, Li Q, Chan AB, Kuo TW and Xue CJ (2020). Fully nested neural network for adaptive compression and quantization. 01-01-2020
2019
Liu Z, Yu L, Hsiao JH and Chan AB (2019). Parametric Manifold Learning of Gaussian Mixture Models. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. 01-08-2019