| Is Physics Informed Loss Always Suitable for Training Physics Informed Neural Network? C Wang, S Li, D He, L Wang Advances in Neural Information Processing Systems 35, 8278-8290, 2022 | 72 | 2022 |
| Your transformer may not be as powerful as you expect S Luo, S Li, S Zheng, TY Liu, L Wang, D He Advances in Neural Information Processing Systems 35, 4301-4315, 2022 | 49 | 2022 |
| Stable, fast and accurate: Kernelized attention with relative positional encoding S Luo, S Li, T Cai, D He, D Peng, S Zheng, G Ke, L Wang, TY Liu NeurIPS 2021, 2021 | 49 | 2021 |
| Functional Interpolation for Relative Positions Improves Long Context Transformers S Li, C You, G Guruganesh, J Ainslie, S Ontanon, M Zaheer, S Sanghai, ... arXiv preprint arXiv:2310.04418, 2023 | 30 | 2023 |
| Learning physics-informed neural networks without stacked back-propagation D He, S Li, W Shi, X Gao, J Zhang, J Bian, L Wang, TY Liu International Conference on Artificial Intelligence and Statistics, 3034-3047, 2023 | 23 | 2023 |
| Can vision transformers perform convolution? S Li, X Chen, D He, CJ Hsieh arXiv preprint arXiv:2111.01353, 2021 | 21 | 2021 |
| Inference scaling laws: An empirical analysis of compute-optimal inference for problem-solving with language models Y Wu, Z Sun, S Li, S Welleck, Y Yang arXiv preprint arXiv:2408.00724, 2024 | 19* | 2024 |
| Learning a fourier transform for linear relative positional encodings in transformers K Choromanski, S Li, V Likhosherstov, KA Dubey, S Luo, D He, Y Yang, ... International Conference on Artificial Intelligence and Statistics, 2278-2286, 2024 | 6 | 2024 |
