| A survey on few-shot class-incremental learning S Tian, L Li, W Li, H Ran, X Ning, P Tiwari Neural Networks 169, 307-324, 2024 | 104 | 2024 |
| Continuous transfer of neural network representational similarity for incremental learning S Tian, W Li, X Ning, H Ran, H Qin, P Tiwari Neurocomputing 545, 126300, 2023 | 51 | 2023 |
| Transformer-based model for symbolic regression via joint supervised learning W Li, W Li, L Sun, M Wu, L Yu, J Liu, Y Li, S Tian The Eleventh International Conference on Learning Representations, 2022 | 24 | 2022 |
| Learning optimal inter-class margin adaptively for few-shot class-incremental learning via neural collapse-based meta-learning H Ran, W Li, L Li, S Tian, X Ning, P Tiwari Information Processing & Management 61 (3), 103664, 2024 | 19 | 2024 |
| Image defect detection and segmentation algorithm of solar cell based on convolutional neural network S Tian, W Li, S Li, G Tian, L Sun, X Ning 2021 6th International Conference on Intelligent Computing and Signal …, 2021 | 18 | 2021 |
| Brain-inspired fast-and slow-update prompt tuning for few-shot class-incremental learning H Ran, X Gao, L Li, W Li, S Tian, G Wang, H Shi, X Ning IEEE Transactions on Neural Networks and Learning Systems, 2024 | 7 | 2024 |
| Pl-fscil: Harnessing the power of prompts for few-shot class-incremental learning S Tian, L Li, W Li, H Ran, L Li, X Ning arXiv preprint arXiv:2401.14807, 2024 | 6 | 2024 |
| Prompt-based learning for few-shot class-incremental learning J Yuan, H Chen, S Tian, W Li, L Li, E Ning, Y Zhang Alexandria Engineering Journal 120, 287-295, 2025 | | 2025 |