| Network In Network M Lin, Q Chen, S Yan International Conference on Learning Representations, 2013 | 9899 | 2013 |
| Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems T Chen, M Li, Y Li, M Lin, N Wang, M Wang, T Xiao, B Xu, C Zhang, ... | 2904 | 2015 |
| On the spectral bias of neural networks N Rahaman, A Baratin, D Arpit, F Draxler, M Lin, F Hamprecht, Y Bengio, ... International conference on machine learning, 5301-5310, 2019 | 1504 | 2019 |
| Gradient based sample selection for online continual learning R Aljundi, M Lin, B Goujaud, Y Bengio Advances in neural information processing systems 32, 2019 | 951 | 2019 |
| HCP: A Flexible CNN Framework for Multi-label Image Classification Y Wei, W Xia, M Lin, J Huang, B Ni, J Dong, Y Zhao, S Yan IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015 | 705 | 2015 |
| Online continual learning with maximal interfered retrieval R Aljundi, E Belilovsky, T Tuytelaars, L Charlin, M Caccia, M Lin, ... Advances in neural information processing systems 32, 2019 | 607 | 2019 |
| NUS-PRO: A New Visual Tracking Challenge A Li, M Lin, Y Wu, MH Yang, S Yan IEEE transactions on pattern analysis and machine intelligence 38, 335--349, 2016 | 219 | 2016 |
| Better diffusion models further improve adversarial training Z Wang, T Pang, C Du, M Lin, W Liu, S Yan International Conference on Machine Learning, 36246-36263, 2023 | 203 | 2023 |
| Online fast adaptation and knowledge accumulation (osaka): a new approach to continual learning M Caccia, P Rodriguez, O Ostapenko, F Normandin, M Lin, ... Advances in Neural Information Processing Systems 33, 16532-16545, 2020 | 159* | 2020 |
| Causal attention for interpretable and generalizable graph classification Y Sui, X Wang, J Wu, M Lin, X He, TS Chua Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 154 | 2022 |
| On evaluating adversarial robustness of large vision-language models Y Zhao, T Pang, C Du, X Yang, C Li, NMM Cheung, M Lin Advances in Neural Information Processing Systems 36, 2024 | 150 | 2024 |
| Robustness and accuracy could be reconcilable by (proper) definition T Pang, M Lin, X Yang, J Zhu, S Yan International Conference on Machine Learning, 17258-17277, 2022 | 141 | 2022 |
| Lorahub: Efficient cross-task generalization via dynamic lora composition C Huang, Q Liu, BY Lin, T Pang, C Du, M Lin arXiv preprint arXiv:2307.13269, 2023 | 123 | 2023 |
| A recipe for watermarking diffusion models Y Zhao, T Pang, C Du, X Yang, NM Cheung, M Lin arXiv preprint arXiv:2303.10137, 2023 | 110 | 2023 |
| Correntropy induced l2 graph for robust subspace clustering C Lu, J Tang, M Lin, L Lin, S Yan, Z Lin Proceedings of the IEEE International Conference on Computer Vision, 1801--1808, 2013 | 104 | 2013 |
| Causal representation learning for out-of-distribution recommendation W Wang, X Lin, F Feng, X He, M Lin, TS Chua Proceedings of the ACM Web Conference 2022, 3562-3571, 2022 | 98 | 2022 |
| How should pre-trained language models be fine-tuned towards adversarial robustness? X Dong, AT Luu, M Lin, S Yan, H Zhang Advances in Neural Information Processing Systems 34, 4356-4369, 2021 | 57 | 2021 |
| Envpool: A highly parallel reinforcement learning environment execution engine J Weng, M Lin, S Huang, B Liu, D Makoviichuk, V Makoviychuk, Z Liu, ... Advances in Neural Information Processing Systems 35, 22409-22421, 2022 | 52 | 2022 |
| Programming a Pavlovian-like conditioning circuit in Escherichia coli H Zhang, M Lin, H Shi, W Ji, L Huang, X Zhang, S Shen, R Gao, S Wu, ... Nature communications 5 (1), 3102, 2014 | 52 | 2014 |
| Bag of tricks for training data extraction from language models W Yu, T Pang, Q Liu, C Du, B Kang, Y Huang, M Lin, S Yan International Conference on Machine Learning, 40306-40320, 2023 | 40 | 2023 |
Tianyu PangSenior Research Scientist, Sea AI Lab在 sea.com 的电子邮件经过验证
