| Disan: Directional self-attention network for rnn/cnn-free language understanding T Shen, T Zhou, G Long, J Jiang, S Pan, C Zhang AAAI 2018, arXiv preprint arXiv:1709.04696, 2017 | 970 | 2017 |
| Godec: Randomized low-rank & sparse matrix decomposition in noisy case T Zhou, D Tao International Conference on Machine Learning 3, 2, 2011 | 921 | 2011 |
| Fedproto: Federated prototype learning across heterogeneous clients Y Tan, G Long, L Liu, T Zhou, Q Lu, J Jiang, C Zhang Proceedings of the AAAI conference on artificial intelligence 36 (8), 8432-8440, 2022 | 891 | 2022 |
| Hallusionbench: an advanced diagnostic suite for entangled language hallucination and visual illusion in large vision-language models T Guan, F Liu, X Wu, R Xian, Z Li, X Liu, X Wang, L Chen, F Huang, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 687* | 2024 |
| Trustllm: Trustworthiness in large language models Y Huang, L Sun, H Wang, S Wu, Q Zhang, Y Li, C Gao, Y Huang, W Lyu, ... arXiv preprint arXiv:2401.05561, 2024 | 643* | 2024 |
| Multi-center federated learning M Xie, G Long, T Shen, T Zhou, X Wang, J Jiang, C Zhang arXiv preprint arXiv:2005.01026, 2021 | 558* | 2021 |
| Rethinking 1d-cnn for time series classification: A stronger baseline W Tang, G Long, L Liu, T Zhou, J Jiang, M Blumenstein ICLR 2022, arXiv preprint arXiv:2002.10061, 1-7, 2020 | 451* | 2020 |
| A survey on knowledge distillation of large language models X Xu, M Li, C Tao, T Shen, R Cheng, J Li, C Xu, D Tao, T Zhou arXiv preprint arXiv:2402.13116, 2024 | 425 | 2024 |
| Alpagasus: Training a better alpaca with fewer data L Chen, S Li, J Yan, H Wang, K Gunaratna, V Yadav, Z Tang, V Srinivasan, ... arXiv preprint arXiv:2307.08701, 2023 | 412 | 2023 |
| Structure-augmented text representation learning for efficient knowledge graph completion B Wang, T Shen, G Long, T Zhou, Y Wang, Y Chang Proceedings of the web conference 2021, 1737-1748, 2021 | 362 | 2021 |
| From quantity to quality: Boosting llm performance with self-guided data selection for instruction tuning M Li, Y Zhang, Z Li, J Chen, L Chen, N Cheng, J Wang, T Zhou, J Xiao Proceedings of the 2024 Conference of the North American Chapter of the …, 2024 | 314 | 2024 |
| Federated learning from pre-trained models: A contrastive learning approach Y Tan, G Long, J Ma, L Liu, T Zhou, J Jiang Advances in Neural Information Processing Systems (NeurIPS 2022), arXiv …, 2022 | 308 | 2022 |
| Curriculum-guided hindsight experience replay M Fang, T Zhou, Y Du, L Han, Z Zhang NeurIPS 2019, 2019 | 230 | 2019 |
| Manifold elastic net: a unified framework for sparse dimension reduction T Zhou, D Tao, X Wu Data Mining and Knowledge Discovery 22 (3), 340-371, 2011 | 212 | 2011 |
| Bi-directional block self-attention for fast and memory-efficient sequence modeling T Shen, T Zhou, G Long, J Jiang, C Zhang ICLR 2018, 2018 | 198 | 2018 |
| Reinforced self-attention network: a hybrid of hard and soft attention for sequence modeling T Shen, T Zhou, G Long, J Jiang, S Wang, C Zhang arXiv preprint arXiv:1801.10296 (accepted by IJCAI 2018), 2018 | 185 | 2018 |
| Diverse client selection for federated learning via submodular maximization R Balakrishnan, T Li, T Zhou, N Himayat, V Smith, J Bilmes International Conference on Learning Representations, 2022 | 183 | 2022 |
| Robust curriculum learning: from clean label detection to noisy label self-correction T Zhou, S Wang, J Bilmes International conference on learning representations, 2020 | 161 | 2020 |
| Instructzero: Efficient instruction optimization for black-box large language models L Chen, J Chen, T Goldstein, H Huang, T Zhou arXiv preprint arXiv:2306.03082, 2023 | 136 | 2023 |
| Personalized federated learning with structure F Chen, G Longr, Z Wu, T Zhou, J Jiang arXiv preprint arXiv:2203.00829, IJCAI 2022 (oral), 2022 | 127* | 2022 |
Guodong LongAssociate Professor, Faculty of Engineering and IT, University of Technology SydneyVerified email at uts.edu.au
