| Beyond gaussian pyramid: Multi-skip feature stacking for action recognition Z Lan, M Lin, X Li, AG Hauptmann, B Raj Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 338 | 2015 |
| Feature interaction augmented sparse learning for fast kinect motion detection X Chang, Z Ma, M Lin, Y Yang, AG Hauptmann IEEE transactions on image processing 26 (8), 3911-3920, 2017 | 188 | 2017 |
| Exploring semantic inter-class relationships (sir) for zero-shot action recognition C Gan, M Lin, Y Yang, Y Zhuang, AG Hauptmann Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015 | 100 | 2015 |
| Concepts not alone: Exploring pairwise relationships for zero-shot video activity recognition C Gan, M Lin, Y Yang, G De Melo, AG Hauptmann Thirtieth AAAI conference on artificial intelligence, 2016 | 74 | 2016 |
| Informedia@ trecvid 2014 med and mer SI Yu, L Jiang, Z Mao, X Chang, X Du, C Gan, Z Lan, Z Xu, X Li, Y Cai, ... NIST TRECVID Video Retrieval Evaluation Workshop 24, 2014 | 37 | 2014 |
| Online kernel learning with a near optimal sparsity bound L Zhang, J Yi, R Jin, M Lin, X He International Conference on Machine Learning, 621-629, 2013 | 33 | 2013 |
| A non-convex one-pass framework for generalized factorization machine and rank-one matrix sensing M Lin, J Ye Advances in Neural Information Processing Systems 29, 2016 | 23 | 2016 |
| Dependent online kernel learning with constant number of random fourier features Z Hu, M Lin, C Zhang IEEE transactions on neural networks and learning systems 26 (10), 2464-2476, 2015 | 23 | 2015 |
| A general framework for transfer sparse subspace learning S Yang, M Lin, C Hou, C Zhang, Y Wu Neural Computing and Applications 21 (7), 1801-1817, 2012 | 23 | 2012 |
| Big data analytical approaches to the NACC dataset: aiding preclinical trial enrichment M Lin, P Gong, T Yang, J Ye, RL Albin, HH Dodge Alzheimer disease and associated disorders 32 (1), 18, 2018 | 21 | 2018 |
| Knapsack pruning with inner distillation Y Aflalo, A Noy, M Lin, I Friedman, L Zelnik arXiv preprint arXiv:2002.08258, 2020 | 19 | 2020 |
| On the sample complexity of random fourier features for online learning: How many random fourier features do we need? M Lin, S Weng, C Zhang ACM Transactions on Knowledge Discovery from Data (TKDD) 8 (3), 1-19, 2014 | 18 | 2014 |
| Margin based PU learning T Gong, G Wang, J Ye, Z Xu, M Lin Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 14 | 2018 |
| Handcrafted local features are convolutional neural networks Z Lan, SI Yu, M Lin, B Raj, AG Hauptmann arXiv preprint arXiv:1511.05045, 2015 | 14 | 2015 |
| The best of both worlds: Combining data-independent and data-driven approaches for action recognition Z Lan, SI Yu, D Yao, M Lin, B Raj, A Hauptmann Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 12 | 2016 |
| Self-paced convolutional neural network for computer aided detection in medical imaging analysis X Li, A Zhong, M Lin, N Guo, M Sun, A Sitek, J Ye, J Thrall, Q Li International Workshop on Machine Learning in Medical Imaging, 212-219, 2017 | 11 | 2017 |
| Online kernel learning with nearly constant support vectors M Lin, L Zhang, R Jin, S Weng, C Zhang Neurocomputing 179, 26-36, 2016 | 10 | 2016 |
| Kvt: k-nn attention for boosting vision transformers P Wang, X Wang, F Wang, M Lin, S Chang, W Xie, H Li, R Jin arXiv preprint arXiv:2106.00515, 2021 | 9 | 2021 |
| Learning accurate entropy model with global reference for image compression Y Qian, Z Tan, X Sun, M Lin, D Li, Z Sun, H Li, R Jin arXiv preprint arXiv:2010.08321, 2020 | 9 | 2020 |
| Robust gaussian process regression for real-time high precision GPS signal enhancement M Lin, X Song, Q Qian, H Li, L Sun, S Zhu, R Jin Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 8 | 2019 |
