Stochastic configuration networks with block increments for data modeling in process industries. - Wei Dai 004; Depeng Li 001; Ping Zhou 003; Tianyou Chai (Inf. Sci., 2019)

Hybrid Parallel Stochastic Configuration Networks for Industrial Data Analytics. - Wei Dai 004; Xinyu Zhou; Depeng Li 001; Song Zhu; Xuesong Wang 001 (IEEE Trans. Ind. Informatics, 2022)

Improving Transferability of Adversarial Point Clouds with Model Commonalities. - Junting Lv; Lianguang Liu; Yutong Zhang; Depeng Li 001; Zhigang Zeng (CBS, 2023)

IF2Net: Innately Forgetting-Free Networks for Continual Learning. - Depeng Li 001; Tianqi Wang; Bingrong Xu; Kenji Kawaguchi; Zhigang Zeng; Ponnuthurai Nagaratnam Suganthan (CoRR, 2023)

Complementary Learning Subnetworks for Parameter-Efficient Class-Incremental Learning. - Depeng Li 001; Zhigang Zeng (CoRR, 2023)

CRNet: A Fast Continual Learning Framework With Random Theory. - Depeng Li 001; Zhigang Zeng (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Towards Continual Learning Desiderata via HSIC-Bottleneck Orthogonalization and Equiangular Embedding. - Depeng Li 001; Tianqi Wang; Junwei Chen; Qining Ren; Kenji Kawaguchi; Zhigang Zeng (AAAI, 2024)

Harnessing Neural Unit Dynamics for Effective and Scalable Class-Incremental Learning. - Depeng Li 001; Tianqi Wang; Junwei Chen; Wei Dai 004; Zhigang Zeng (ICML, 2024)

Multi-view class incremental learning. - Depeng Li 001; Tianqi Wang; Junwei Chen; Kenji Kawaguchi; Cheng Lian; Zhigang Zeng (Inf. Fusion, 2024)

Multiple semantic prompt for rehearsal-free continual learning. - Junwei Chen; Zhenyu Zhang 009; Depeng Li 001; Zhigang Zeng (Neural Networks, 2025)

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning. - Tianqi Wang; Jingcai Guo; Depeng Li 001; Zhi Chen 010 (CoRR, 2025)

Complementary Learning Subnetworks Towards Parameter-Efficient Class-Incremental Learning. - Depeng Li 001; Zhigang Zeng; Wei Dai 004; Ponnuthurai Nagaratnam Suganthan (IEEE Trans. Knowl. Data Eng., 2025)