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2020 – today
- 2023
[j3]Shi-Xin Zhang
, Jonathan Allcock, Zhou-Quan Wan, Shuo Liu, Jiace Sun, Hao Yu, Xing-Han Yang, Jiezhong Qiu, Zhaofeng Ye, Yu-Qin Chen, Chee-Kong Lee, Yicong Zheng, Shao-Kai Jian, Hong Yao, Chang-Yu Hsieh, Shengyu Zhang:
TensorCircuit: a Quantum Software Framework for the NISQ Era. Quantum 7: 912 (2023)- [j2]Zaixi Zhang, Qi Liu, Zhenya Huang, Hao Wang, Chee-Kong Lee, Enhong Chen:
Model Inversion Attacks Against Graph Neural Networks. IEEE Trans. Knowl. Data Eng. 35(9): 8729-8741 (2023) - [i8]Zaixi Zhang, Qi Liu, Chee-Kong Lee, Chang-Yu Hsieh, Enhong Chen:
An Equivariant Generative Framework for Molecular Graph-Structure Co-Design. CoRR abs/2304.12436 (2023) - 2022
- [j1]Yu-Qin Chen, Yu Chen, Chee-Kong Lee, Shengyu Zhang, Chang-Yu Hsieh:
Optimizing quantum annealing schedules with Monte Carlo tree search enhanced with neural networks. Nat. Mach. Intell. 4(3): 269-278 (2022) - [c5]Zaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu, Cheekong Lee:
ProtGNN: Towards Self-Explaining Graph Neural Networks. AAAI 2022: 9127-9135 - [c4]Zaixi Zhang, Qi Liu, Qingyong Hu, Chee-Kong Lee:
Hierarchical Graph Transformer with Adaptive Node Sampling. NeurIPS 2022 - [c3]Shao-Hen Chiew, Leong-Chuan Kwek, Chee-Kong Lee:
Exploring the Dynamics of Quantum Information in Many-Body Localised Systems with High Performance Computing. SCFA 2022: 43-58 - [i7]Jonathan Wei Zhong Lau, Hui Zhang, Lingxiao Wan, Liang Shi, Hong Cai, Xianshu Luo, Patrick Lo, Chee-Kong Lee, Leong-Chuan Kwek, Ai Qun Liu:
A photonic chip-based machine learning approach for the prediction of molecular properties. CoRR abs/2203.02285 (2022) - [i6]Zaixi Zhang, Qi Liu, Zhenya Huang, Hao Wang, Chee-Kong Lee, Enhong Chen:
Model Inversion Attacks against Graph Neural Networks. CoRR abs/2209.07807 (2022) - [i5]Zaixi Zhang, Qi Liu, Qingyong Hu, Chee-Kong Lee:
Hierarchical Graph Transformer with Adaptive Node Sampling. CoRR abs/2210.03930 (2022) - 2021
- [c2]Zaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu, Chee-Kong Lee:
Motif-based Graph Self-Supervised Learning for Molecular Property Prediction. NeurIPS 2021: 15870-15882 - [i4]Zaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu, Cheekong Lee:
Motif-based Graph Self-Supervised Learning for Molecular Property Prediction. CoRR abs/2110.00987 (2021) - [i3]Zaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu, Cheekong Lee:
ProtGNN: Towards Self-Explaining Graph Neural Networks. CoRR abs/2112.00911 (2021) - 2020
- [c1]Zhongkai Hao, Chengqiang Lu, Zhenya Huang, Hao Wang, Zheyuan Hu, Qi Liu, Enhong Chen, Cheekong Lee:
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction. KDD 2020: 731-752 - [i2]Zhongkai Hao, Chengqiang Lu, Zheyuan Hu, Hao Wang, Zhenya Huang, Qi Liu, Enhong Chen, Cheekong Lee:
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction. CoRR abs/2007.03196 (2020)
2010 – 2019
- 2019
- [i1]Guangyong Chen, Pengfei Chen, Chang-Yu Hsieh, Chee-Kong Lee, Benben Liao, Renjie Liao, Weiwen Liu, Jiezhong Qiu, Qiming Sun, Jie Tang, Richard S. Zemel, Shengyu Zhang:
Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models. CoRR abs/1906.09427 (2019)
Coauthor Index
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