| Subspace adversarial training T Li, Y Wu, S Chen, K Fang, X Huang CVPR 2022 oral, 13409-13418, 2022 | 79 | 2022 |
| Low dimensional trajectory hypothesis is true: Dnns can be trained in tiny subspaces T Li, L Tan, Z Huang, Q Tao, Y Liu, X Huang IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (3), 3411-3420, 2022 | 45* | 2022 |
| Trainable weight averaging: Efficient training by optimizing historical solutions T Li, Z Huang, Q Tao, Y Wu, X Huang The Eleventh International Conference on Learning Representations, 2022 | 19* | 2022 |
| Friendly sharpness-aware minimization T Li, P Zhou, Z He, X Cheng, X Huang IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2024), 2024 | 12 | 2024 |
| Towards robust neural networks via orthogonal diversity K Fang, Q Tao, Y Wu, T Li, J Cai, F Cai, X Huang, J Yang Pattern Recognition 149, 110281, 2024 | 8* | 2024 |
| Investigating catastrophic overfitting in fast adversarial training: a self-fitting perspective Z He, T Li, S Chen, X Huang Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 8 | 2023 |
| PromptIntern: Saving Inference Costs by Internalizing Recurrent Prompt during Large Language Model Fine-tuning J Zou, M Zhou, T Li, S Han, D Zhang EMNLP 2024 Findings, 2024 | 6 | 2024 |
| Efficient generalization improvement guided by random weight perturbation T Li, W Yan, Z Lei, Y Wu, K Fang, M Yang, X Huang arXiv preprint arXiv:2211.11489, 2022 | 6 | 2022 |
| Low-dimensional gradient helps out-of-distribution detection Y Wu, T Li, X Cheng, J Yang, X Huang IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024 | 4 | 2024 |
| Revisiting Random Weight Perturbation for Efficiently Improving Generalization T Li, Q Tao, W Yan, Z Lei, Y Wu, K Fang, M He, X Huang Transactions on Machine Learning Research, 2024 | 4 | 2024 |
| On multi-head ensemble of smoothed classifiers for certified robustness K Fang, Q Tao, Y Wu, T Li, X Huang, J Yang arXiv preprint arXiv:2211.10882, 2022 | 4 | 2022 |
| Learning scalable model soup on a single gpu: An efficient subspace training strategy T Li, W Jiang, F Liu, X Huang, JT Kwok European Conference on Computer Vision, 342-359, 2024 | 3* | 2024 |
| Towards Natural Machine Unlearning Z He, T Li, X Cheng, Z Huang, X Huang arXiv preprint arXiv:2405.15495, 2024 | 2 | 2024 |
| Better Loss Landscape Visualization for Deep Neural Networks with Trajectory Information R Ding, T Li, X Huang Asian Conference on Machine Learning, 311-326, 2024 | 2 | 2024 |
| Online Continual Learning via Logit Adjusted Softmax Z Huang, T Li, C Yuan, Y Wu, X Huang Transactions on Machine Learning Research, 2023 | 2 | 2023 |
| Trainable weight averaging: A general approach for subspace training T Li, Z Huang, Y Wu, Z He, Q Tao, X Huang, CJ Lin arXiv preprint arXiv:2205.13104, 2022 | 1 | 2022 |
| Unified Gradient-Based Machine Unlearning with Remain Geometry Enhancement Z Huang, X Cheng, JH Zheng, H Wang, Z He, T Li, X Huang NeurIPS 2024 (Spotlight), 2024 | | 2024 |
| Flat-LoRA: Low-Rank Adaption over a Flat Loss Landscape T Li, Z He, Y Li, Y Wang, L Shang, X Huang arXiv preprint arXiv:2409.14396, 2024 | | 2024 |
| SS-ADA: A Semi-Supervised Active Domain Adaptation Framework for Semantic Segmentation W Yan, Y Qian, Y Li, T Li, C Wang, M Yang arXiv preprint arXiv:2407.12788, 2024 | | 2024 |
| Defending Against Similarity Shift Attack for EaaS via Adaptive Multi-Target Watermarking Z Yang, P Chen, T Li, K Liu, Y Huang, X Lin Information Sciences, 120893, 2024 | | 2024 |
Xiaolin Huang (黄晓霖)Professor, Shanghai Jiao Tong UniversityZweryfikowany adres z sjtu.edu.cn