Guolin Ke
Guolin Ke
DP Technology
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Cited by
Cited by
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
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
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
Rethinking Positional Encoding in Language Pre-training
G Ke, D He, TY Liu
International Conference on Learning Representations (ICLR) 2021, 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
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
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
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
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
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
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
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
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
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
TabNN: A universal neural network solution for tabular data
G Ke, J Zhang, Z Xu, J Bian, TY Liu
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
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
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
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
First place solution of kdd cup 2021 & ogb large-scale challenge graph prediction track
C Ying, M Yang, S Zheng, G Ke, S Luo, T Cai, C Wu, Y Wang, Y Shen, ...
arXiv preprint arXiv:2106.08279, 2021
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