| Do Transformers Really Perform Badly for Graph Representation? C Ying, T Cai, S Luo, S Zheng, G Ke, D He, Y Shen, TY Liu Thirty-Fifth Conference on Neural Information Processing Systems (NIPS), 2021, 2021 | 1425 | 2021 |
| On layer normalization in the transformer architecture R Xiong, Y Yang, D He, K Zheng, S Zheng, C Xing, H Zhang, Y Lan, ... Proceedings of the 37th International Conference on Machine Learning, 2020, 2020 | 1112 | 2020 |
| Asynchronous stochastic gradient descent with delay compensation S Zheng, Q Meng, T Wang, W Chen, N Yu, ZM Ma, TY Liu Proceedings of the 34th International Conference on Machine Learning, PMLR …, 2017 | 379* | 2017 |
| Invertible Image Rescaling M Xiao, S Zheng, C Liu, Y Wang, D He, G Ke, J Bian, Z Lin, TY Liu European Conference on Computer Vision (ECCV) 2020, 126-144, 2020 | 278 | 2020 |
| Cross-Iteration Batch Normalization Z Yao, Y Cao, S Zheng, G Huang, S Lin IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021, 2020 | 142 | 2020 |
| One transformer can understand both 2d & 3d molecular data S Luo, T Chen, Y Xu, S Zheng, TY Liu, L Wang, D He The Eleventh International Conference on Learning Representations, 2022 | 102 | 2022 |
| Deep learning for prediction of the air quality response to emission changes J Xing, S Zheng, D Ding, JT Kelly, S Wang, S Li, T Qin, M Ma, Z Dong, ... Environmental science & technology 54 (14), 8589-8600, 2020 | 94 | 2020 |
| Predicting equilibrium distributions for molecular systems with deep learning S Zheng, J He, C Liu, Y Shi, Z Lu, W Feng, F Ju, J Wang, J Zhu, Y Min, ... Nature Machine Intelligence, 1-10, 2024 | 84* | 2024 |
| Benchmarking graphormer on large-scale molecular modeling datasets Y Shi, S Zheng, G Ke, Y Shen, J You, J He, S Luo, C Liu, D He, TY Liu arXiv preprint arXiv:2203.04810, 2022 | 70 | 2022 |
| How could Neural Networks understand Programs? D Peng, S Zheng, Y Li, G Ke, D He, TY Liu Proceedings of International Conference on Machine Learning (ICML), 2021 …, 2021 | 68 | 2021 |
| 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 | 52 | 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 Advances in Neural Information Processing Systems, 2021 (NeurIPS 2021), 2021 | 51 | 2021 |
| Molecule generation for target protein binding with structural motifs Z Zhang, Y Min, S Zheng, Q Liu The Eleventh International Conference on Learning Representations, 2023 | 48 | 2023 |
| The impact of large language models on scientific discovery: a preliminary study using gpt-4 MR AI4Science, MA Quantum arXiv preprint arXiv:2311.07361, 2023 | 38 | 2023 |
| -SGD: Optimizing ReLU Neural Networks in its Positively Scale-Invariant Space Q Meng, S Zheng, H Zhang, W Chen, Q Ye, ZM Ma, TY Liu Proceedings of the 7th International Conference on Learning Representations …, 2018 | 36 | 2018 |
| Invertible rescaling network and its extensions M Xiao, S Zheng, C Liu, Z Lin, TY Liu International Journal of Computer Vision 131 (1), 134-159, 2023 | 29 | 2023 |
| Quantized training of gradient boosting decision trees Y Shi, G Ke, Z Chen, S Zheng, TY Liu Advances in neural information processing systems 35, 18822-18833, 2022 | 27 | 2022 |
| Modeling Lost Information in Lossy Image Compression Y Wang, M Xiao, C Liu, S Zheng, TY Liu arXiv preprint arXiv:2006.11999, 2020 | 27 | 2020 |
| Capacity control of relu neural networks by basis-path norm S Zheng, Q Meng, H Zhang, W Chen, N Yu, TY Liu Proceedings of the 33rd AAAI Conference on Artificial Intelligence, 2019, 2018 | 25 | 2018 |
| Mimicking atmospheric photochemical modeling with a deep neural network J Xing, S Zheng, S Li, L Huang, X Wang, JT Kelly, S Wang, C Liu, C Jang, ... Atmospheric research 265, 105919, 2022 | 18 | 2022 |
Tie-Yan LiuPresident, Zhongguancun Academy | IEEE Fellow | ACM Fellow | AAIA FellowVerified email at bjzgca.edu.cn