Recursive Feature Selection Based on Minimum Redundancy Maximum Relevancy. - Yuansheng Yang; Haiyan Li; Xiaohui Lin 002; Di Ming (PAAP, 2010)

Feedback Number of Generalized Kautz Digraphs GK(2, n). - Lei Wang; Xirong Xu; Yuansheng Yang; Di Ming; Xuezhi Dong (Ars Comb., 2014)

A Probabilistic Derivation of LASSO and L12-Norm Feature Selections. - Di Ming; Chris Ding; Feiping Nie 001 (AAAI, 2019)

Robust Flexible Feature Selection via Exclusive L21 Regularization. - Di Ming; Chris Ding (IJCAI, 2019)

ViT-P: Rethinking Data-efficient Vision Transformers from Locality. - Bin Chen; Ran Wang; Di Ming; Xin Feng (CoRR, 2022)

TRM-UAP: Enhancing the Transferability of Data-Free Universal Adversarial Perturbation via Truncated Ratio Maximization. - Yiran Liu; Xin Feng; Yunlong Wang; Wu Yang; Di Ming (ICCV, 2023)

Transferable Structural Sparse Adversarial Attack Via Exact Group Sparsity Training. - Di Ming; Peng Ren; Yunlong Wang; Xin Feng (CVPR, 2024)

Boosting the Transferability of Adversarial Attack on Vision Transformer with Adaptive Token Tuning. - Di Ming; Peng Ren; Yunlong Wang; Xin Feng (NeurIPS, 2024)

Label-aware Dual-view Graph Neural Network for Protein-Protein Interaction Classification. - Xiaofei Zhu; Xinsheng Wang; Yanyan Lan; Xin Feng; Xiaoyang Liu 001; Di Ming (Expert Syst. Appl., 2024)

STFormer: An efficient visual Transformer model with sparse attention and adaptive token aggregation. - Xin Feng; Sheng Zhong; Yunlong Wang; Di Ming (Pattern Recognit. Lett., 2025)