Learn Robust Features via Orthogonal Multi-Path. - Kun Fang 004; Yingwen Wu; Tao Li 054; Xiaolin Huang; Jie Yang 002 (CoRR, 2020)

Train Deep Neural Networks in 40-D Subspaces. - Tao Li 054; Lei Tan; Qinghua Tao; Yipeng Liu 001; Xiaolin Huang (CoRR, 2021)

Subspace Adversarial Training. - Tao Li 054; Yingwen Wu; Sizhe Chen; Kun Fang 004; Xiaolin Huang (CVPR, 2022)

Trainable Weight Averaging for Fast Convergence and Better Generalization. - Tao Li 054; Zhehao Huang; Qinghua Tao; Yingwen Wu; Xiaolin Huang (CoRR, 2022)

Efficient Generalization Improvement Guided by Random Weight Perturbation. - Tao Li 054; Weihao Yan; Zehao Lei; Yingwen Wu; Kun Fang 004; Ming Yang; Xiaolin Huang (CoRR, 2022)

Trainable Weight Averaging: Efficient Training by Optimizing Historical Solutions. - Tao Li 054; Zhehao Huang; Qinghua Tao; Yingwen Wu; Xiaolin Huang (ICLR, 2023)

Low Dimensional Trajectory Hypothesis is True: DNNs Can Be Trained in Tiny Subspaces. - Tao Li 054; Lei Tan; Zhehao Huang; Qinghua Tao; Yipeng Liu 001; Xiaolin Huang (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Better Generalization in Fast Training: Flat Trainable Weight in Subspace. - Zehao Lei; Yingwen Wu; Tao Li 054 (ICMLC, 2024)

Towards robust neural networks via orthogonal diversity. - Kun Fang 004; Qinghua Tao; Yingwen Wu; Tao Li 054; Jia Cai; Feipeng Cai; Xiaolin Huang; Jie Yang 002 (Pattern Recognit., 2024)

Revisiting Random Weight Perturbation for Efficiently Improving Generalization. - Tao Li 054; Qinghua Tao; Weihao Yan; Yingwen Wu; Zehao Lei; Kun Fang 004; Mingzhen He; Xiaolin Huang (Trans. Mach. Learn. Res., 2024)

Low-Dimensional Gradient Helps Out-of-Distribution Detection. - Yingwen Wu; Tao Li 054; Xinwen Cheng; Jie Yang 002; Xiaolin Huang (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Multi-head ensemble of smoothed classifiers for certified robustness. - Kun Fang 004; Qinghua Tao; Yingwen Wu; Tao Li 054; Xiaolin Huang; Jie Yang 002 (Neural Networks, 2025)