| Deep learning in bioinformatics: Introduction, application, and perspective in the big data era Y Li, C Huang, L Ding, Z Li, Y Pan, X Gao Methods 166, 4-21, 2019 | 349 | 2019 |
| Multi-class learning: From theory to algorithm J Li, Y Liu, R Yin, H Zhang, L Ding, W Wang Advances in Neural Information Processing Systems 31, 2018 | 52 | 2018 |
| Fast cross-validation for kernel-based algorithms Y Liu, S Liao, S Jiang, L Ding, H Lin, W Wang IEEE transactions on pattern analysis and machine intelligence 42 (5), 1083-1096, 2019 | 43 | 2019 |
| On the decision boundary of deep neural networks Y Li, L Ding, X Gao arXiv preprint arXiv:1808.05385, 2018 | 41 | 2018 |
| Supportnet: solving catastrophic forgetting in class incremental learning with support data Y Li, Z Li, L Ding, Y Pan, C Huang, Y Hu, W Chen, X Gao arXiv preprint arXiv:1806.02942, 2018 | 37 | 2018 |
| Sail: Self-augmented graph contrastive learning L Yu, S Pei, L Ding, J Zhou, L Li, C Zhang, X Zhang Proceedings of the AAAI Conference on Artificial Intelligence 36 (8), 8927-8935, 2022 | 35 | 2022 |
| Approximate model selection for large scale LSSVM L Ding, S Liao Asian Conference on Machine Learning, 165-180, 2011 | 33 | 2011 |
| An approximate approach to automatic kernel selection L Ding, S Liao IEEE Transactions on Cybernetics 47 (3), 554-565, 2016 | 24 | 2016 |
| Dynamically visual disambiguation of keyword-based image search Y Yao, Z Sun, F Shen, L Liu, L Wang, F Zhu, L Ding, G Wu, L Shao arXiv preprint arXiv:1905.10955, 2019 | 21 | 2019 |
| Fast Cross-Validation. Y Liu, H Lin, L Ding, W Wang, S Liao IJCAI, 2497-2503, 2018 | 20 | 2018 |
| Approximate kernel selection via matrix approximation L Ding, S Liao, Y Liu, L Liu, F Zhu, Y Yao, L Shao, X Gao IEEE Transactions on Neural Networks and Learning Systems 31 (11), 4881-4891, 2020 | 19 | 2020 |
| Randomized kernel selection with spectra of multilevel circulant matrices L Ding, S Liao, Y Liu, P Yang, X Gao Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 15 | 2018 |
| Systematic selection of chemical fingerprint features improves the Gibbs energy prediction of biochemical reactions M Alazmi, H Kuwahara, O Soufan, L Ding, X Gao Bioinformatics 35 (15), 2634-2643, 2019 | 12 | 2019 |
| Linear kernel tests via empirical likelihood for high-dimensional data L Ding, Z Liu, Y Li, S Liao, Y Liu, P Yang, G Yu, L Shao, X Gao Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 3454-3461, 2019 | 11 | 2019 |
| Model selection with the covering number of the ball of RKHS L Ding, S Liao Proceedings of the 23rd ACM International Conference on Conference on …, 2014 | 10 | 2014 |
| Nyström approximate model selection for LSSVM L Ding, S Liao Advances in Knowledge Discovery and Data Mining: 16th Pacific-Asia …, 2012 | 10 | 2012 |
| Approximate consistency: Towards foundations of approximate kernel selection L Ding, S Liao Machine Learning and Knowledge Discovery in Databases: European Conference …, 2014 | 9 | 2014 |
| KMA-𝛼: a kernel matrix approximation algorithm for support vector machines L Ding, S Liao Computer Research and Development 49 (4), 746-753, 2012 | 9 | 2012 |
| Learning with uncertain kernel matrix set L Jia, SZ Liao, LZ Ding Journal of Computer Science and Technology 25 (4), 709-727, 2010 | 9 | 2010 |
| Predictive Nyström method for kernel methods J Wu, L Ding, S Liao Neurocomputing 234, 116-125, 2017 | 8 | 2017 |
Xin GaoProfessor, Computer Sciences; Director, Computational Bioscience Research CenterDirección de correo verificada de kaust.edu.sa
