Optimization of Spectrum-Energy Efficiency in Heterogeneous Communication Network. - Fangqing Gu; Ziquan Liu; Yiu-ming Cheung; Hai-Lin Liu 001 (SEAL, 2017)

Parametric Manifold Learning of Gaussian Mixture Models. - Ziquan Liu; Lei Yu 013; Janet H. Hsiao; Antoni B. Chan (IJCAI, 2019)

Fully Nested Neural Network for Adaptive Compression and Quantization. - Yufei Cui; Ziquan Liu; Wuguannan Yao; Qiao Li 001; Antoni B. Chan; Tei-Wei Kuo; Chun Jason Xue (IJCAI, 2020)

Improve Generalization and Robustness of Neural Networks via Weight Scale Shifting Invariant Regularizations. - Ziquan Liu; Yufei Cui; Antoni B. Chan (CoRR, 2020)

Bayesian Nested Neural Networks for Uncertainty Calibration and Adaptive Compression. - Yufei Cui; Ziquan Liu; Qiao Li 001; Antoni B. Chan; Chun Jason Xue (CVPR, 2021)

A Generalized Loss Function for Crowd Counting and Localization. - Jia Wan 001; Ziquan Liu; Antoni B. Chan (CVPR, 2021)

Feature Pyramid Hierarchy based DeltaNet Network for Insulator Defect Detection. - Ziquan Liu; Nan Yao; Xi Wu; Jinggang Yang; Hai Xue (DASC/PiCom/CBDCom/CyberSciTech, 2021)

Detection of Transmission Line Corridors Risk Intrusion based on B-CNN. - Nan Yao; Ziquan Liu; Zhen Wang; Yongling Lu; Hai Xue (DASC/PiCom/CBDCom/CyberSciTech, 2021)

Bolt Defect Detection Based on CenterNet Model. - Nan Yao; Yuxi Zhao; Xi Wu; Ziquan Liu; Jianhua Qin (DASC/PiCom/CBDCom/CyberSciTech, 2021)

Target Detection of Engineering Vehicles Based on Co-learning Labels. - Zhen Wang; Nan Yao; Ziquan Liu; Xueqiong Zhu; Hai Xue (HPCC/DSS/SmartCity/DependSys, 2021)

A Deep Image Denoising Method at Transmit Electricity Surveillance Environment. - Nan Yao; Ziquan Liu; Zhen Wang; Yongling Lu; Jianhua Qin (HPCC/DSS/SmartCity/DependSys, 2021)

Maintenance Decision Generator for Electrical Equipment Based on Reinforcement Learning. - Ruifan Li; Zeyuan Wang; Yifan Du; Zepeng Zhai; Yongping Xiong; Ziquan Liu (SPML, 2021)

Bayesian Nested Neural Networks for Uncertainty Calibration and Adaptive Compression. - Yufei Cui; Ziquan Liu; Qiao Li 001; Yu Mao 001; Antoni B. Chan; Chun Jason Xue (CoRR, 2021)

The Implicit Biases of Stochastic Gradient Descent on Deep Neural Networks with Batch Normalization. - Ziquan Liu; Yufei Cui; Jia Wan 001; Yu Mao 001; Antoni B. Chan (CoRR, 2021)

Improved Fine-tuning by Leveraging Pre-training Data: Theory and Practice. - Ziquan Liu; Yi Xu 008; Yuanhong Xu; Qi Qian 001; Hao Li 030; Antoni B. Chan; Rong Jin 001 (CoRR, 2021)

Clustering Hidden Markov Models with Variational Bayesian Hierarchical EM. - Hui Lan; Ziquan Liu; Janet H. Hsiao; Dan Yu; Antoni B. Chan (Zenodo, 2021)

Boosting Adversarial Robustness From The Perspective of Effective Margin Regularization. - Ziquan Liu; Antoni B. Chan (BMVC, 2022)

Improved Fine-Tuning by Better Leveraging Pre-Training Data. - Ziquan Liu; Yi Xu 008; Yuanhong Xu; Qi Qian 001; Hao Li 030; Xiangyang Ji; Antoni B. Chan; Rong Jin 001 (NeurIPS, 2022)

An Empirical Study on Distribution Shift Robustness From the Perspective of Pre-Training and Data Augmentation. - Ziquan Liu; Yi Xu 008; Yuanhong Xu; Qi Qian 001; Hao Li 030; Rong Jin 001; Xiangyang Ji; Antoni B. Chan (CoRR, 2022)

PRIMAL-GMM: PaRametrIc MAnifold Learning of Gaussian Mixture Models. - Ziquan Liu; Lei Yu 013; Janet H. Hsiao; Antoni B. Chan (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization. - Ziquan Liu; Yi Xu 008; Xiangyang Ji; Antoni B. Chan (CVPR, 2023)

DropMAE: Masked Autoencoders with Spatial-Attention Dropout for Tracking Tasks. - Qiangqiang Wu; Tianyu Yang 003; Ziquan Liu; Baoyuan Wu; Ying Shan; Antoni B. Chan (CVPR, 2023)

Bayes-MIL: A New Probabilistic Perspective on Attention-based Multiple Instance Learning for Whole Slide Images. - Yufei Cui; Ziquan Liu; Xiangyu Liu; Xue Liu 001; Cong Wang 001; Tei-Wei Kuo; Chun Jason Xue; Antoni B. Chan (ICLR, 2023)

Retrieval-Augmented Multiple Instance Learning. - Yufei Cui; Ziquan Liu; Yixin Chen; Yuchen Lu; Xinyue Yu; Xue (Steve) Liu; Tei-Wei Kuo; Miguel Rodrigues 001; Chun Jason Xue; Antoni B. Chan (NeurIPS, 2023)

Clustering Hidden Markov Models With Variational Bayesian Hierarchical EM. - Hui Lan; Ziquan Liu; Janet H. Hsiao; Dan Yu; Antoni B. Chan (IEEE Trans. Neural Networks Learn. Syst., 2023)

Variational Nested Dropout. - Yufei Cui; Yu Mao 001; Ziquan Liu; Qiao Li 001; Antoni B. Chan; Xue (Steve) Liu; Tei-Wei Kuo; Chun Jason Xue (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Analysing educational scientific collaboration through multilayer networks: patterns, impact and network generation model. - Shenwen Chen; Yisen Wang 005; Ziquan Liu; Wenbo Du 001; Lei Zheng; Runran Liu (J. Complex Networks, 2023)

A Secure Image Watermarking Framework with Statistical Guarantees via Adversarial Attacks on Secret Key Networks. - Feiyu Chen; Wei Lin 018; Ziquan Liu; Antoni B. Chan (ECCV (40), 2024)

The Second Visual Object Tracking Segmentation VOTS2024 Challenge Results. - Matej Kristan; Jirí Matas; Pavel Tokmakov; Michael Felsberg; Luka Cehovin Zajc; Alan Lukezic; Khanh-Tung Tran; Xuan-Son Vu; Johanna Björklund; Hyung Jin Chang; Gustavo Fernández; Minasadat Attari; et al. (ECCV Workshops (7), 2024)

The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks. - Ziquan Liu; Yufei Cui; Yan Yan 006; Yi Xu 008; Xiangyang Ji; Xue Liu 001; Antoni B. Chan (ICML, 2024)

A gender recognition method based on EEG microstates. - Yanxiang Niu; Xin Chen 080; Yuansen Chen; Zixuan Yao; Xuemei Chen; Ziquan Liu; Xiangyan Meng; Yanqing Liu; Zongya Zhao; Haojun Fan (Comput. Biol. Medicine, 2024)

PROSAC: Provably Safe Certification for Machine Learning Models under Adversarial Attacks. - Chen Feng; Ziquan Liu; Zhuo Zhi; Ilija Bogunovic; Carsten Gerner-Beuerle; Miguel Rodrigues 001 (AAAI, 2025)

Cultural Alignment in Large Language Models: An Explanatory Analysis Based on Hofstede's Cultural Dimensions. - Reem I. Masoud; Ziquan Liu; Martin Ferianc; Philip C. Treleaven; Miguel Rodrigues 001 (COLING, 2025)

Get Confused Cautiously: Textual Sequence Memorization Erasure with Selective Entropy Maximization. - Zhaohan Zhang; Ziquan Liu; Ioannis Patras (COLING, 2025)

AIM-Fair: Advancing Algorithmic Fairness via Selectively Fine-Tuning Biased Models with Contextual Synthetic Data. - Zengqun Zhao; Ziquan Liu; Yu Cao; Shaogang Gong; Ioannis Patras (CVPR, 2025)

Query-based Knowledge Transfer for Heterogeneous Learning Environments. - Norah Alballa; Wenxuan Zhang; Ziquan Liu; Ahmed M. Abdelmoniem; Mohamed Elhoseiny; Marco Canini (ICLR, 2025)

SEBRA : Debiasing through Self-Guided Bias Ranking. - Adarsh Kappiyath; Abhra Chaudhuri; Ajay Kumar Jaiswal; Ziquan Liu; Yunpeng Li; Xiatian Zhu; Lu Yin 006 (ICLR, 2025)

Borrowing treasures from neighbors: In-context learning for multimodal learning with missing modalities and data scarcity. - Zhuo Zhi; Ziquan Liu; Moe Elbadawi; Adam Daneshmend; Mine Orlu; Abdul Basit; Andreas Demosthenous; Miguel R. D. Rodrigues (Neurocomputing, 2025)

TFAR: A Training-Free Framework for Autonomous Reliable Reasoning in Visual Question Answering. - Zhuo Zhi; Chen Feng; Adam Daneshmend; Mine Orlu; Andreas Demosthenous; Lu Yin; Da Li; Ziquan Liu; Miguel R. D. Rodrigues (Trans. Mach. Learn. Res., 2025)

Wasserstein Modality Alignment Makes Your Multimodal Transformer More Robust. - Zhuo Zhi; Yuxuan Sun; Qiangqiang Wu; Ziquan Liu; Miguel R. D. Rodrigues (Trans. Mach. Learn. Res., 2025)

RALAD: Bridging the Real-to-Sim Domain Gap in Autonomous Driving with Retrieval-Augmented Learning. - Jiacheng Zuo; Haibo Hu; Zikang Zhou; Yufei Cui; Ziquan Liu; Jianping Wang 001; Nan Guan; Jin Wang 009; Chun Jason Xue (CoRR, 2025)

Seeing and Reasoning with Confidence: Supercharging Multimodal LLMs with an Uncertainty-Aware Agentic Framework. - Zhuo Zhi; Chen Feng; Adam Daneshmend; Mine Orlu; Andreas Demosthenous; Lu Yin; Da Li; Ziquan Liu; Miguel R. D. Rodrigues (CoRR, 2025)

CASteer: Steering Diffusion Models for Controllable Generation. - Tatiana Gaintseva; Chengcheng Ma; Ziquan Liu; Martin Benning; Gregory G. Slabaugh; Jiankang Deng; Ismail Elezi (CoRR, 2025)

V-STaR: Benchmarking Video-LLMs on Video Spatio-Temporal Reasoning. - Zixu Cheng; Jian Hu; Ziquan Liu; Chenyang Si; Wei Li; Shaogang Gong (CoRR, 2025)

Temporal Unlearnable Examples: Preventing Personal Video Data from Unauthorized Exploitation by Object Tracking. - Qiangqiang Wu; Yi Yu 011; Chenqi Kong; Ziquan Liu; Jia Wan 001; Haoliang Li; Alex C. Kot; Antoni B. Chan (CoRR, 2025)

ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction. - Danhui Chen; Ziquan Liu; Chuxi Yang; Dan Wang; Yan Yan 006; Yi Xu 008; Xiangyang Ji (CoRR, 2025)

Reinforcement Learning for Target Zone Blood Glucose Control. - David H. Mguni; Jing Dong 008; Wanrong Yang; Ziquan Liu; Muhammad Salman Haleem; Baoxiang Wang 001 (CoRR, 2025)