| The application of two-level attention models in deep convolutional neural network for fine-grained image classification T Xiao, Y Xu, K Yang, J Zhang, Y Peng, Z Zhang Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 1069 | 2015 |
| G-eval: NLG evaluation using gpt-4 with better human alignment Y Liu, D Iter, Y Xu, S Wang, R Xu, C Zhu arXiv preprint arXiv:2303.16634, 2023 | 990 | 2023 |
| An empirical study of training end-to-end vision-and-language transformers ZY Dou, Y Xu, Z Gan, J Wang, S Wang, L Wang, C Zhu, P Zhang, L Yuan, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 410 | 2022 |
| Generate rather than retrieve: Large language models are strong context generators W Yu, D Iter, S Wang, Y Xu, M Ju, S Sanyal, C Zhu, M Zeng, M Jiang arXiv preprint arXiv:2209.10063, 2022 | 287 | 2022 |
| Want to reduce labeling cost? GPT-3 can help S Wang, Y Liu, Y Xu, C Zhu, M Zeng arXiv preprint arXiv:2108.13487, 2021 | 267 | 2021 |
| Scale-invariant convolutional neural networks Y Xu, T Xiao, J Zhang, K Yang, Z Zhang arXiv preprint arXiv:1411.6369, 2014 | 180 | 2014 |
| Dialoglm: Pre-trained model for long dialogue understanding and summarization M Zhong, Y Liu, Y Xu, C Zhu, M Zeng Proceedings of the AAAI Conference on Artificial Intelligence 36 (10), 11765 …, 2022 | 143 | 2022 |
| Kg-fid: Infusing knowledge graph in fusion-in-decoder for open-domain question answering D Yu, C Zhu, Y Fang, W Yu, S Wang, Y Xu, X Ren, Y Yang, M Zeng arXiv preprint arXiv:2110.04330, 2021 | 124 | 2021 |
| Training data is more valuable than you think: A simple and effective method by retrieving from training data S Wang, Y Xu, Y Fang, Y Liu, S Sun, R Xu, C Zhu, M Zeng arXiv preprint arXiv:2203.08773, 2022 | 106 | 2022 |
| Revive: Regional visual representation matters in knowledge-based visual question answering Y Lin, Y Xie, D Chen, Y Xu, C Zhu, L Yuan Advances in neural information processing systems 35, 10560-10571, 2022 | 102 | 2022 |
| Fusing context into knowledge graph for commonsense question answering Y Xu, C Zhu, R Xu, Y Liu, M Zeng, X Huang Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 …, 2021 | 90 | 2021 |
| Dict-bert: Enhancing language model pre-training with dictionary W Yu, C Zhu, Y Fang, D Yu, S Wang, Y Xu, M Zeng, M Jiang arXiv preprint arXiv:2110.06490, 2021 | 78 | 2021 |
| Multi-task learning with sample re-weighting for machine reading comprehension Y Xu, X Liu, Y Shen, J Liu, J Gao Proceedings of the 2019 Conference of the North American Chapter of the …, 2019 | 75* | 2019 |
| Human parity on commonsenseqa: Augmenting self-attention with external attention Y Xu, C Zhu, S Wang, S Sun, H Cheng, X Liu, J Gao, P He, M Zeng, ... arXiv preprint arXiv:2112.03254, 2021 | 70 | 2021 |
| Active learning for graph neural networks via node feature propagation Y Wu, Y Xu, A Singh, Y Yang, A Dubrawski arXiv preprint arXiv:1910.07567, 2019 | 70 | 2019 |
| Preference-based reinforcement learning with finite-time guarantees Y Xu, R Wang, L Yang, A Singh, A Dubrawski Advances in Neural Information Processing Systems 33, 18784-18794, 2020 | 67 | 2020 |
| Small models are valuable plug-ins for large language models C Xu, Y Xu, S Wang, Y Liu, C Zhu, J McAuley arXiv preprint arXiv:2305.08848, 2023 | 54 | 2023 |
| Empowering language models with knowledge graph reasoning for question answering Z Hu, Y Xu, W Yu, S Wang, Z Yang, C Zhu, KW Chang, Y Sun arXiv preprint arXiv:2211.08380, 2022 | 53 | 2022 |
| Noise-Tolerant Interactive Learning Using Pairwise Comparisons Y Xu, H Zhang, K Miller, A Singh, A Dubrawski Advances in Neural Information Processing Systems, 2431--2440, 2017 | 51* | 2017 |
| On Strategyproof Conference Peer Review Y Xu, H Zhao, X Shi, NB Shah Proceedings of the Twenty-Eighth International Joint Conference on …, 2019 | 50 | 2019 |
Chenguang ZhuZoomยืนยันอีเมลแล้วที่ zoom.us