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 | 21 | 2023 |
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 | 21 | 2023 |
Self-supervised class incremental learning Z Ni, S Tang, Y Zhuang arXiv preprint arXiv:2111.11208, 2021 | 7 | 2021 |
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 | 7 | 2021 |
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 | 5 | 2021 |
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 | | |