Lin Li

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Michael SpratlingUniversity of Luxembourg and King's College LondonVerified email at uni.lu
Jianing QiuThe Chinese University of Hong KongVerified email at cuhk.edu.hk
Bo XiaoResearch Associate, Imperial College LondonVerified email at imperial.ac.uk
Frank LoImperial College LondonVerified email at imperial.ac.uk
yinzhao dongUniversity of Hong KongVerified email at connect.hku.hk
Kyle LamImperial College LondonVerified email at imperial.ac.uk
Benny LoImperial College LondonVerified email at imperial.ac.uk
Jiachuan PengDPhil student, University of OxfordVerified email at seh.ox.ac.uk
Jiankai SunStanford UniversityVerified email at cs.stanford.edu
Wu Yuan,PhDAssistant Professor in Department of Biomedical Engineering, the Chinese University of Hong Kong.Verified email at cuhk.edu.hk
Dong XuCurators’ Distinguished Professor, University of MissouriVerified email at missouri.edu
Chawin SitawarinPostdoctoral Researcher @ MetaVerified email at meta.com
Haoyan GuanKing’s College LondonVerified email at kcl.ac.uk
Yifei WangMITVerified email at mit.edu

Lin Li

Ph.D. student, King's College London

Verified email at kcl.ac.uk - Homepage

trustworthy ML data-centric ML robustness safety


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Large ai models in health informatics: Applications, challenges, and the future J Qiu, L Li, J Sun, J Peng, P Shi, R Zhang, Y Dong, K Lam, FPW Lo, ... IEEE Journal of Biomedical and Health Informatics (JBHI), 2023	77	2023
Data augmentation alone can improve adversarial training L Li, M Spratling International Conference on Learning Representations (ICLR), 2023	38	2023
Understanding and combating robust overfitting via input loss landscape analysis and regularization L Li, M Spratling Pattern Recognition 136, 109229, 2023	26	2023
Visionfm: a multi-modal multi-task vision foundation model for generalist ophthalmic artificial intelligence J Qiu, J Wu, H Wei, P Shi, M Zhang, Y Sun, L Li, H Liu, H Liu, S Hou, ... arXiv preprint arXiv:2310.04992, 2023	8	2023
One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language Models L Li, H Guan, J Qiu, M Spratling IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024	5	2024
OODRobustBench: benchmarking and analyzing adversarial robustness under distribution shift L Li, Y Wang, C Sitawarin, M Spratling International Conference on Machine Learning (ICML) 2024, ICLRW-DMLR 2024, 2024	3	2024
AROID: Improving Adversarial Robustness through Online Instance-wise Data Augmentation L Li, J Qiu, M Spratling International Journal of Computer Vision (IJCV), 2024	1	2024
Improved Adversarial Training Through Adaptive Instance-wise Loss Smoothing L Li, M Spratling arXiv preprint arXiv:2303.14077, 2023	1	2023
Advancing Robots with Greater Dynamic Dexterity: A Large-Scale Multi-View and Multi-Modal Dataset of Human-Human Throw&Catch of Arbitrary Objects L Chen, J Qiu, L Li*, X Luo, G Chi, Y Zheng International Journal of Robotics Research (IJRR), 2024		2024
OODRobustBench: a benchmark and large-scale analysis of adversarial robustness under distribution shift L Li, Y Wang, C Sitawarin, M Spratling		2024
Towards Robust Visual Classification through Adversarial Training L Li King's College London, 2024		2024
Understanding Deep CNNs via Interpretable Individual Units L Li Imperial College London, 2018		2018

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