| A survey on evaluation of large language models Y Chang, X Wang, J Wang, Y Wu, L Yang, K Zhu, H Chen, X Yi, C Wang, ... ACM Transactions on Intelligent Systems and Technology 15 (3), 1-45, 2024 | 833 | 2024 |
| Freematch: Self-adaptive thresholding for semi-supervised learning Y Wang, H Chen, Q Heng, W Hou, Y Fan, Z Wu, J Wang, M Savvides, ... Internation Conference on Learning Representation (ICLR), 2023 | 191 | 2023 |
| On the robustness of chatgpt: An adversarial and out-of-distribution perspective J Wang, X Hu, W Hou, H Chen, R Zheng, Y Wang, L Yang, H Huang, ... ICLR 2023 workshop on reliable large model, 2023 | 177 | 2023 |
| Promptbench: Towards evaluating the robustness of large language models on adversarial prompts K Zhu, J Wang, J Zhou, Z Wang, H Chen, Y Wang, L Yang, W Ye, Y Zhang, ... arXiv preprint arXiv:2306.04528, 2023 | 147 | 2023 |
| Softmatch: Addressing the quantity-quality trade-off in semi-supervised learning H Chen, R Tao, Y Fan, Y Wang, J Wang, B Schiele, X Xie, B Raj, ... Internation Conference on Learning Representation (ICLR), 2023 | 111 | 2023 |
| PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization Y Wang, Z Yu, Z Zeng, L Yang, C Wang, H Chen, C Jiang, R Xie, J Wang, ... Internation Conference on Learning Representation (ICLR), 2024 | 95 | 2024 |
| Usb: A unified semi-supervised learning benchmark for classification Y Wang, H Chen, Y Fan, W Sun, R Tao, W Hou, R Wang, L Yang, Z Zhou, ... Advances in Neural Information Processing Systems 35, 3938-3961, 2022 | 95 | 2022 |
| 3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised Learning X Xu, H Chen, F Moreno-Noguer, LA Jeni, F De la Torre European Conference on Computer Vision, 284-300, 2020 | 57 | 2020 |
| Conv-adapter: Exploring parameter efficient transfer learning for convnets H Chen, R Tao, H Zhang, Y Wang, X Li, W Ye, J Wang, G Hu, M Savvides Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 36 | 2024 |
| 3D human pose, shape and texture from low-resolution images and videos X Xu, H Chen, F Moreno-Noguer, LA Jeni, F De la Torre IEEE transactions on pattern analysis and machine intelligence 44 (9), 4490-4504, 2021 | 36 | 2021 |
| Competeai: Understanding the competition behaviors in large language model-based agents Q Zhao, J Wang, Y Zhang, Y Jin, K Zhu, H Chen, X Xie ICML 2024, 2023 | 19 | 2023 |
| Exploring vision-language models for imbalanced learning Y Wang, Z Yu, J Wang, Q Heng, H Chen, W Ye, R Xie, X Xie, S Zhang International Journal of Computer Vision 132 (1), 224-237, 2024 | 18 | 2024 |
| Adversarial Large-Scale Root Gap Inpainting H Chen, M Valerio Giuffrida, P Doerner, SA Tsaftaris Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 17 | 2019 |
| Root gap correction with a deep inpainting model H Chen, MV Giuffrida, P Doerner, SA Tsaftaris | 16 | 2018 |
| Promptbench: A unified library for evaluation of large language models K Zhu, Q Zhao, H Chen, J Wang, X Xie arXiv preprint arXiv:2312.07910, 2023 | 11 | 2023 |
| Blind inpainting of large-scale masks of thin structures with adversarial and reinforcement learning H Chen, MV Giuffrida, P Doerner, SA Tsaftaris arXiv preprint arXiv:1912.02470, 2019 | 10 | 2019 |
| Unitail: detecting, reading, and matching in retail scene F Chen, H Zhang, Z Li, J Dou, S Mo, H Chen, Y Zhang, U Ahmed, C Zhu, ... European Conference on Computer Vision, 705-722, 2022 | 9 | 2022 |
| Imprecise label learning: A unified framework for learning with various imprecise label configurations H Chen, A Shah, J Wang, R Tao, Y Wang, X Xie, M Sugiyama, R Singh, ... arXiv preprint arXiv:2305.12715, 2023 | 7 | 2023 |
| On catastrophic inheritance of large foundation models H Chen, B Raj, X Xie, J Wang arXiv preprint arXiv:2402.01909, 2024 | 6 | 2024 |
| Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks H Chen, J Wang, A Shah, R Tao, H Wei, X Xie, M Sugiyama, B Raj Internation Conference on Learning Representation (ICLR), 2024 | 5 | 2024 |
Jindong WangMicrosoft Research Asia在 microsoft.com 的电子邮件经过验证
