| Unsupervised Paraphrasing by Simulated Annealing X Liu, L Mou, F Meng, H Zhou, J Zhou, S Song ACL, 302–312, 2020 | 86 | 2020 |
| Deep geometric representations for modeling effects of mutations on protein-protein binding affinity X Liu, Y Luo, P Li, S Song, J Peng PLoS computational biology 17 (8), e1009284, 2021 | 78* | 2021 |
| Contrastive learning of subject-invariant EEG representations for cross-subject emotion recognition X Shen, X Liu, X Hu, D Zhang, S Song IEEE Transactions on Affective Computing, 2022 | 70 | 2022 |
| TrimNet: learning molecular representation from triplet messages for biomedicine P Li, Y Li, CY Hsieh, S Zhang, X Liu, H Liu, S Song, X Yao Briefings in Bioinformatics 22 (4), bbaa266, 2021 | 54 | 2021 |
| Addressnet: Shift-based primitives for efficient convolutional neural networks Y He, X Liu, H Zhong, Y Ma 2019 IEEE Winter conference on applications of computer vision (WACV), 1213-1222, 2019 | 47* | 2019 |
| Finding decision jumps in text classification X Liu, L Mou, H Cui, Z Lu, S Song Neurocomputing 371, 177-187, 2020 | 30 | 2020 |
| Jumper: Learning when to make classification decisions in reading X Liu, L Mou, H Cui, Z Lu, S Song IJCAI, 4237-4243, 2018 | 23 | 2018 |
| Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks P Li, J Wang, Z Li, Y Qiao, X Liu, F Ma, P Gao, S Song, G Xie IJCAI, 2694-2700, 2021 | 18 | 2021 |
| Simulated annealing for optimization of graphs and sequences X Liu, P Li, F Meng, H Zhou, H Zhong, J Zhou, L Mou, S Song Neurocomputing 465, 310-324, 2021 | 14 | 2021 |
| A chance-constrained generative framework for sequence optimization X Liu, Q Liu, S Song, J Peng International Conference on Machine Learning, 6271-6281, 2020 | 11 | 2020 |
| Object-oriented neural programming (oonp) for document understanding Z Lu, X Liu, H Cui, Y Yan, D Zheng ACL, 2717–2726, 2018 | 11 | 2018 |
| Gpt-nas: Neural architecture search with the generative pre-trained model C Yu, X Liu, C Tang, W Feng, J Lv arXiv preprint arXiv:2305.05351, 2023 | 7 | 2023 |
| Abstract Rule Learning for Paraphrase Generation. X Liu, W Lei, J Lv, J Zhou, LD Raedt IJCAI, 4273-4279, 2022 | 6 | 2022 |
| Riboexp: an interpretable reinforcement learning framework for ribosome density modeling H Hu, X Liu, A Xiao, YY Li, C Zhang, T Jiang, D Zhao, S Song, J Zeng Briefings in Bioinformatics 22 (5), bbaa412, 2021 | 6* | 2021 |
| Decomposing Retrosynthesis into Reactive Center Prediction and Molecule Generation X Liu, P Li, S Song bioRxiv, 677849, 2019 | 5 | 2019 |
| Searching safe landing site with parameters optimization based on genetic algorithm X Liu, H Zhong, S Chang, Y Meng 2014 11th International Computer Conference on Wavelet Actiev Media …, 2014 | 5 | 2014 |
| Learning robust rule representations for abstract reasoning via internal inferences W Zhang, S Mo, X Liu, S Song Advances in Neural Information Processing Systems 35, 33550-33562, 2022 | 4 | 2022 |
| Powerformer: A temporal-based transformer model for wind power forecasting S Mo, H Wang, B Li, Z Xue, S Fan, X Liu Energy Reports 11, 736-744, 2024 | 2 | 2024 |
| Semantical information graph model toward fast information valuation in large teamwork Y Zhang, Y Xu, H Hu, X Liu ECAI 2014, 1137-1138, 2014 | 2 | 2014 |
| MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting W Cai, Y Liang, X Liu, J Feng, Y Wu Proceedings of the AAAI Conference on Artificial Intelligence 38 (10), 11141 …, 2024 | 1 | 2024 |
Pengyong LiXidian UniversityDirección de correo verificada de xidian.edu.cn
