| LightGBM: A Highly Efficient Gradient Boosting Decision Tree G Ke, Q Meng, T Finley, T Wang, W Chen, W Ma, Q Ye, TY Liu Advances in Neural Information Processing Systems, 3148-3156, 2017 | 14106 | 2017 |
| Do transformers really perform badly for graph representation? C Ying, T Cai, S Luo, S Zheng, G Ke, D He, Y Shen, TY Liu Advances in neural information processing systems 34, 28877-28888, 2021 | 1175 | 2021 |
| Deep subdomain adaptation network for image classification Y Zhu, F Zhuang, J Wang, G Ke, J Chen, J Bian, H Xiong, Q He IEEE transactions on neural networks and learning systems 32 (4), 1713-1722, 2020 | 808 | 2020 |
| Rethinking Positional Encoding in Language Pre-training G Ke, D He, TY Liu International Conference on Learning Representations (ICLR) 2021, 2020 | 271 | 2020 |
| Invertible image rescaling M Xiao, S Zheng, C Liu, Y Wang, D He, G Ke, J Bian, Z Lin, TY Liu Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 247 | 2020 |
| Uni-Mol: A Universal 3D Molecular Representation Learning Framework G Zhou, Z Gao, Q Ding, H Zheng, H Xu, Z Wei, L Zhang, G Ke International Conference on Learning Representations, 2023 | 207 | 2023 |
| DeepGBM: A deep learning framework distilled by GBDT for online prediction tasks G Ke, Z Xu, J Zhang, J Bian, TY Liu Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 162 | 2019 |
| A communication-efficient parallel algorithm for decision tree Q Meng, G Ke, T Wang, W Chen, Q Ye, ZM Ma, TY Liu Advances in Neural Information Processing Systems, 1279-1287, 2016 | 162 | 2016 |
| Less is more: Pre-train a strong text encoder for dense retrieval using a weak decoder S Lu, D He, C Xiong, G Ke, W Malik, Z Dou, P Bennett, T Liu, A Overwijk arXiv preprint arXiv:2102.09206, 2021 | 80 | 2021 |
| How could neural networks understand programs? D Peng, S Zheng, Y Li, G Ke, D He, TY Liu International Conference on Machine Learning, 8476-8486, 2021 | 60 | 2021 |
| 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 | 59 | 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 34, 22795-22807, 2021 | 46 | 2021 |
| Uni-Fold: an open-source platform for developing protein folding models beyond AlphaFold Z Li, X Liu, W Chen, F Shen, H Bi, G Ke, L Zhang bioRxiv, 2022.08. 04.502811, 2022 | 40 | 2022 |
| Metro: Efficient denoising pretraining of large scale autoencoding language models with model generated signals P Bajaj, C Xiong, G Ke, X Liu, D He, S Tiwary, TY Liu, P Bennett, X Song, ... arXiv preprint arXiv:2204.06644, 2022 | 38 | 2022 |
| TabNN: A universal neural network solution for tabular data G Ke, J Zhang, Z Xu, J Bian, TY Liu | 36* | 2018 |
| Do deep learning models really outperform traditional approaches in molecular docking? Y Yu, S Lu, Z Gao, H Zheng, G Ke arXiv preprint arXiv:2302.07134, 2023 | 31 | 2023 |
| Taking notes on the fly helps language pre-training Q Wu, C Xing, Y Li, G Ke, D He, TY Liu International Conference on Learning Representations, 2021 | 28* | 2021 |
| Transformers with competitive ensembles of independent mechanisms A Lamb, D He, A Goyal, G Ke, CF Liao, M Ravanelli, Y Bengio arXiv preprint arXiv:2103.00336, 2021 | 20 | 2021 |
| Mc-bert: Efficient language pre-training via a meta controller Z Xu, L Gong, G Ke, D He, S Zheng, L Wang, J Bian, TY Liu arXiv preprint arXiv:2006.05744, 2020 | 17 | 2020 |
| DPA-2: Towards a universal large atomic model for molecular and material simulation D Zhang, X Liu, X Zhang, C Zhang, C Cai, H Bi, Y Du, X Qin, J Huang, B Li, ... arXiv preprint arXiv:2312.15492, 2023 | 16 | 2023 |
Tie-Yan LiuDistinguished Scientist, Microsoft Research AI4Science | IEEE Fellow | ACM Fellow | AAIA FellowVerified email at microsoft.com
