| Sparseadapter: An easy approach for improving the parameter-efficiency of adapters S He, L Ding, D Dong, M Zhang, D Tao EMNLP 2022 Findings, 2022 | 80 | 2022 |
| Vega-mt: The jd explore academy translation system for wmt22 C Zan, K Peng, L Ding, B Qiu, B Liu, S He, Q Lu, Z Zhang, C Liu, W Liu, ... Seventh Conference on Machine Translation (WMT22), 2022 | 56 | 2022 |
| Superfiltering: Weak-to-strong data filtering for fast instruction-tuning M Li, Y Zhang, S He, Z Li, H Zhao, J Wang, N Cheng, T Zhou arXiv preprint arXiv:2402.00530, 2024 | 38 | 2024 |
| Reflection-tuning: Data recycling improves llm instruction-tuning M Li, L Chen, J Chen, S He, H Huang, J Gu, T Zhou arXiv preprint arXiv:2310.11716, 2023 | 26* | 2023 |
| Selective reflection-tuning: Student-selected data recycling for llm instruction-tuning M Li, L Chen, J Chen, S He, J Gu, T Zhou arXiv preprint arXiv:2402.10110, 2024 | 20 | 2024 |
| Reformatted alignment RZ Fan, X Li, H Zou, J Li, S He, E Chern, J Hu, P Liu arXiv preprint arXiv:2402.12219, 2024 | 14 | 2024 |
| Mera: Merging pretrained adapters for few-shot learning S He, RZ Fan, L Ding, L Shen, T Zhou, D Tao arXiv preprint arXiv:2308.15982, 2023 | 13 | 2023 |
| Merging experts into one: Improving computational efficiency of mixture of experts S He, RZ Fan, L Ding, L Shen, T Zhou, D Tao EMNLP 2023 Oral, 2023 | 12 | 2023 |
| PAD-Net: An Efficient Framework for Dynamic Networks S He, L Ding, D Dong, B Liu, F Yu, D Tao ACL 2023, 2023 | 12* | 2023 |
| Demystifying the Compression of Mixture-of-Experts Through a Unified Framework S He, D Dong, L Ding, A Li arXiv preprint arXiv:2406.02500, 2024 | 10 | 2024 |
| Accurate prediction of antibody function and structure using bio-inspired antibody language model H Jing, Z Gao, S Xu, T Shen, Z Peng, S He, T You, S Ye, W Lin, S Sun Briefings in Bioinformatics 25 (4), bbae245, 2024 | 9 | 2024 |
| Loki: Low-Rank Keys for Efficient Sparse Attention P Singhania, S Singh, S He, S Feizi, A Bhatele arXiv preprint arXiv:2406.02542, 2024 | 8 | 2024 |
| What matters in transformers? not all attention is needed S He, G Sun, Z Shen, A Li arXiv preprint arXiv:2406.15786, 2024 | 7 | 2024 |
| Sd-conv: Towards the parameter-efficiency of dynamic convolution S He, C Jiang, D Dong, L Ding Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2023 | 4 | 2023 |
| Multi-modal Attention Network for Stock Movements Prediction S He, S Gu The AAAI-22 Workshop on Knowledge Discovery from Unstructured Data in …, 2021 | 3 | 2021 |
| RESSA: Repair Sparse Vision-Language Models via Sparse Cross-Modality Adaptation S He, T Chen arXiv preprint arXiv:2404.02424, 2024 | 1 | 2024 |
| NeuralSlice: Neural 3D triangle mesh reconstruction via slicing 4D tetrahedral meshes C Jiang, J Yang, S He, Y Lai, L Gao | 1 | 2023 |
| Towards counterfactual fairness thorough auxiliary variables B Tian, Z Wang, S He, W Ye, G Sun, Y Dai, Y Wu, A Li arXiv preprint arXiv:2412.04767, 2024 | | 2024 |
| Fair Diagnosis: Leveraging Causal Modeling to Mitigate Medical Bias B Tian, Y He, M Liu, Y Dai, Z Wang, S He, G Sun, Z Shen, W Ye, Y Wu, ... arXiv preprint arXiv:2412.04739, 2024 | | 2024 |
| Router-Tuning: A Simple and Effective Approach for Enabling Dynamic-Depth in Transformers S He, T Ge, G Sun, B Tian, X Wang, A Li, D Yu arXiv preprint arXiv:2410.13184, 2024 | | 2024 |
Liang DingUniversity of SydneyE-mail megerősítve itt: sydney.edu.au