| Stochastic configuration networks with block increments for data modeling in process industries W Dai, D Li, P Zhou, T Chai Information Sciences 484, 367-386, 2019 | 77 | 2019 |
| Hybrid parallel stochastic configuration networks for industrial data analytics W Dai, X Zhou, D Li, S Zhu, X Wang IEEE Transactions on Industrial Informatics 18 (4), 2331-2341, 2021 | 55 | 2021 |
| Data driven particle size estimation of hematite grinding process using stochastic configuration network with robust technique W Dai, D Li, Q Chen, T Chai Journal of Central South University 26 (1), 43-62, 2019 | 11 | 2019 |
| CRNet: A Fast Continual Learning Framework With Random Theory D Li, Z Zeng IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (9), 10731 …, 2023 | 7 | 2023 |
| A model and data hybrid parallel learning method for stochastic configuration networks W Dai, D Li, C Yang, X Ma Acta Automatica Sinica 47 (10), 2427-2437, 2021 | 7* | 2021 |
| Multi-view class incremental learning D Li, T Wang, J Chen, K Kawaguchi, C Lian, Z Zeng Information Fusion 102, 102021, 2024 | 4 | 2024 |
| Complementary learning subnetworks for parameter-efficient class-incremental learning D Li, Z Zeng arXiv preprint arXiv:2306.11967, 2023 | 3 | 2023 |
| IF2Net: Innately forgetting-free networks for continual learning D Li, T Wang, B Xu, K Kawaguchi, Z Zeng, PN Suganthan arXiv preprint arXiv:2306.10480, 2023 | 2 | 2023 |
| Towards Continual Learning Desiderata via HSIC-Bottleneck Orthogonalization and Equiangular Embedding D Li, T Wang, J Chen, Q Ren, K Kawaguchi, Z Zeng Proceedings of the AAAI Conference on Artificial Intelligence 38 (12), 13464 …, 2024 | | 2024 |
