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Zixuan Ni
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Degeneration-tuning: Using scrambled grid shield unwanted concepts from stable diffusion
Z Ni, L Wei, J Li, S Tang, Y Zhuang, Q Tian
Proceedings of the 31st ACM International Conference on Multimedia, 8900-8909, 2023
212023
Continual vision-language representation learning with off-diagonal information
Z Ni, L Wei, S Tang, Y Zhuang, Q Tian
International Conference on Machine Learning, 26129-26149, 2023
212023
Self-supervised class incremental learning
Z Ni, S Tang, Y Zhuang
arXiv preprint arXiv:2111.11208, 2021
72021
Revisiting catastrophic forgetting in class incremental learning
Z Ni, H Shi, S Tang, L Wei, Q Tian, Y Zhuang
arXiv preprint arXiv:2107.12308, 2021
72021
Alleviate representation overlapping in class incremental learning by contrastive class concentration
Z Ni, H Shi, S Tang, Y Zhuang
arXiv preprint arXiv:2107.12308 1 (2), 2021
52021
Comparing ECG Lead Subsets for Heart Arrhythmia/ECG Pattern Classification: Convolutional Neural Networks and Random Forest
S Reznichenko, J Whitaker, Z Ni, S Zhou
CJC Open, 2024
2024
E-CGL: An Efficient Continual Graph Learner
J Guo, Z Ni, Y Zhu, S Tang
arXiv preprint arXiv:2408.09350, 2024
2024
Comparative analysis of deep learning and conventional machine learning for heart arrhythmias/ECG pattern classification using optimal ECG lead sets
S Zhou, S Reznichenko, J Whitaker, Z Ni
Journal of Electrocardiology 85, 17, 2024
2024
A Simple Data-Parameters Balancing Framework for Early Ventricular Activation Origin Localization
Z Ni, A AbdelWahab, J Sapp, S Zhou
Comparative Analysis of Optimal ECG-Lead Subsets for Arrhythmia/ECG Pattern Classification: Deep Learning Versus Conventional Methods
S Reznichenko, J Whitaker, Z Ni, S Zhou
Shijie, Comparative Analysis of Optimal ECG-Lead Subsets for Arrhythmia/ECG …, 0
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