Fine perceptive gans for brain mr image super-resolution in wavelet domain S You, Y Liu, B Lei, S Wang IEEE Transactions on Neural Networks and Learning Systems, 2022 | 138 | 2022 |
A survey on model compression for large language models X Zhu, J Li, Y Liu, C Ma, W Wang arXiv preprint arXiv:2308.07633, 2023 | 96 | 2023 |
Morphological feature visualization of Alzheimer's disease via Multidirectional Perception GAN WYBLYSSWYLZFYHMK Ng IEEE Transactions on Neural Networks and Learning Systems, 2022 | 90 | 2022 |
Learning entity and relation embeddings for knowledge resolution H Lin, Y Liu, W Wang, Y Yue, Z Lin Procedia Computer Science 108, 345-354, 2017 | 88 | 2017 |
Efficient approximation of cross-validation for kernel methods using Bouligand influence function Y Liu, S Jiang, S Liao International conference on machine learning, 324-332, 2014 | 66 | 2014 |
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 (NIPS), 2018 | 53 | 2018 |
Deep Safe Incomplete Multi-view Clustering: Theorem and Algorithm T Huayi, Y Liu* Proceedings of the 39th International Conference on Machine Learning (ICML), 2022 | 43* | 2022 |
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, 2020 | 43 | 2020 |
Semantic-aware dehazing network with adaptive feature fusion S Zhang, W Ren, X Tan, ZJ Wang, Y Liu, J Zhang, X Zhang, X Cao IEEE Transactions on Cybernetics 53 (1), 454-467, 2021 | 39 | 2021 |
Can large language models empower molecular property prediction? C Qian, H Tang, Z Yang, H Liang, Y Liu arXiv preprint arXiv:2307.07443, 2023 | 35 | 2023 |
Deep Safe Multi-view Clustering: Reducing the Risk of Clustering Performance Degradation Caused by View Increase H Tang, Y Liu CVPR, 2022 | 30 | 2022 |
Granularity selection for cross-validation of SVM Y Liu, S Liao Information Sciences 378, 475-483, 2017 | 30 | 2017 |
Learning Structural Representations via Dynamic Object Landmarks Discovery for Sketch Recognition and Retrieval H Zhang, P She, Y Liu, X Cao, H Foroosh IEEE Transactions on Image Processing, 2019 | 29 | 2019 |
Infinite kernel learning: generalization bounds and algorithms Y Liu, S Liao, H Lin, Y Yue, W Wang Proceedings of the AAAI Conference on artificial intelligence 31 (1), 2017 | 29 | 2017 |
Sharper Generalization Bounds for Clustering S Li, Y Liu Proceedings of the 28th International Conference on Machine Learning (ICML), 2021 | 27 | 2021 |
Eigenvalues ratio for kernel selection of kernel methods Y Liu, S Liao Proceedings of the AAAI Conference on artificial intelligence 29 (1), 2015 | 26 | 2015 |
Effective Distributed Learning with Random Features: Improved Bounds and Algorithms Y Liu, J Liu, S Wang International Conference on Learning Representations (ICLR), 2021 | 23 | 2021 |
Learning kernels with upper bounds of leave-one-out error Y Liu, S Liao, Y Hou Proceedings of the 20th ACM International Conference on Information and …, 2011 | 23 | 2011 |
Sketch kernel ridge regression using circulant matrix: Algorithm and theory R Yin, Y Liu, W Wang, D Meng IEEE transactions on neural networks and learning systems 31 (9), 3512-3524, 2019 | 22 | 2019 |
Divide-and-conquer learning with nyström: Optimal rate and algorithm R Yin, Y Liu, L Lu, W Wang, D Meng Proceedings of the AAAI conference on artificial intelligence 34 (04), 6696-6703, 2020 | 21 | 2020 |