| Kola: Carefully benchmarking world knowledge of large language models J Yu, X Wang, S Tu, S Cao, D Zhang-Li, X Lv, H Peng, Z Yao, X Zhang, ... arXiv preprint arXiv:2306.09296, 2023 | 115 | 2023 |
| When does in-context learning fall short and why? a study on specification-heavy tasks H Peng, X Wang, J Chen, W Li, Y Qi, Z Wang, Z Wu, K Zeng, B Xu, L Hou, ... arXiv preprint arXiv:2311.08993, 2023 | 23 | 2023 |
| When does in-context learning fall short and why H Peng, X Wang, J Chen, W Li, Y Qi, Z Wang, Z Wu, K Zeng, B Xu, L Hou, ... A study on specification-heavy tasks. CoRR, abs/2311.08993, 2023 | 6 | 2023 |
| Efficient task transfer for HLS DSE Z Ding, A Sohrabizadeh, W Li, Z Qin, Y Sun, J Cong arXiv preprint arXiv:2408.13270, 2024 | 3 | 2024 |
| Fast Inference of Removal-Based Node Influence W Li, Z Xiao, X Luo, Y Sun Proceedings of the ACM Web Conference 2024, 422-433, 2024 | 1 | 2024 |
| Hierarchical Mixture of Experts: Generalizable Learning for High-Level Synthesis W Li, D Wang, Z Ding, A Sohrabizadeh, Z Qin, J Cong, Y Sun arXiv preprint arXiv:2410.19225, 2024 | | 2024 |
| Learning to Compare Hardware Designs for High-Level Synthesis Y Bai, A Sohrabizadeh, Z Ding, R Liang, W Li, D Wang, H Ren, Y Sun, ... Proceedings of the 2024 ACM/IEEE International Symposium on Machine Learning …, 2024 | | 2024 |
| Rethinking the setting of semi-supervised learning on graphs Z Li, M Ding, W Li, Z Wang, Z Zeng, Y Cen, J Tang arXiv preprint arXiv:2205.14403, 2022 | | 2022 |
| Evaluating Task-Specific Node Influence via Node-Removal-Based Fast Graph Neural Network Inference W Li, Z Xiao, X Luo, Y Sun The Web Conference 2024, 0 | | |