![]() ![]() Yu-Feng Li, Ivor Tsang, James Kwok and Zhi-Hua Zhou. IEEE Transactions on Pattern Analysis and Machine Intelligence ( TPAMI), 37(1):175-188, 2015. Towards Making Unlabeled Data Never Hurt. Classifier Circle Method for Multi-Label Learning. Instance Selection Method for Improving Graph-Based Semi-Supervised Learning. Learning Safe Multi-Label Prediction for Weakly Labeled Data. Tong Wei*, Lan-Zhe Guo, Yu-Feng Li, Wei Gao. Safe Semi-Supervised Learning: A Brief Introduction. IEEE Transactions on Knowledge and Data Engineering ( TKDE). Robust Multi-Label Learning with PRO Loss. IEEE Transactions on Neural Network and Learning Systems ( TNNLS), 31(7): 2315-2324, 2020. Does Tail Label Help for Large-Scale Multi-Label Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence ( TPAMI), 43(1): 334-346, 2021. IEEE Transactions on Knowledge and Data Engineering ( TKDE), 33(5): 2071-2082, 2021. Lightweight Label Propagation for Large-Scale Network Data. IEEE Transactions on Visualization and Computer Graphics ( TVCG), 27(9): 3701-3716, 2021 Interactive Graph Construction for Graph-Based Semi-Supervised Learning. ![]() Science CHINA Information Science, 65: 212101, 2022.Ĭhangjian Chen, Zhaowei Wang, Jing Wu, Xiting Wang, Lan-Zhe Guo, Yu-Feng Li, Shixia Liu. Robust Model Selection for PU Learning under Constraint. Tong Wei*, Hai Wang, Wei-Wei Tu, Yu-Feng Li. Science CHINA Information Science (In chinese), In Press. Science CHINA Information Science, In Press. LAMDA-SSL: A Comprehensive Semi-Supervised Learning Toolkit. Lin-Han Jia*, Lan-Zhe Guo, Zhi Zhou, Yu-Feng Li. Towards Robust Test-Time Adaptation for Open-Set Recognition. Zhi Zhou*, Ding-Chu Zhang, Yu-Feng Li, Min-Ling Zhang. RTS: Learning Robustly from Time Series Data with Noisy Label. Residual Diverse Ensemble for Long-Tailed Multi-Label Text Classification. Book Chapter of 'Machine Learning and its Applications' 2015ĪCML 2022 Journal Track Guest Editors: Yu-Feng Li, Prateek Jain, Machine Learning Journal 2023ĪCML 2021 Journal Track Guest Editors: Yu-Feng Li, Mehmet Gonen, Kee-Eung Kim, Machine Learning Journal 2022 Min-Ling Zhang, Qing-Hua Hu and Yu-Feng Li. ![]() LaWGPT: We open-sourced a LLM for Chinese Legal domain. LAMDA-SSL contains 30+ semi-supervised learning algorithms, including both statiscal and deep semi-supervised learning. Maritime claims under the United Nations Convention on the Law of the Sea, ancient maps of China made by Chinese authorities, Chinese individuals or foreigners,Īnd ancient maps of the Philippines made by Westerners, Philippine authorities or individuals, vividly present the actual historical facts in the South China Sea.LAMDA-SSL: We provide a comprehensive and easy-to-use toolkit for semi-supervised learning. Yet, this is exactly what China did in 1947 when China drew its nine- dashed line map in the South China Sea, claiming as basis historical facts.ĭespite the irrelevance of historical facts to present-day Thus, a state cannot enlarge its rights under international law by its own unilateral acts or domestic legislation. Official Chinese Map of the South China Sea with the nine-dotted line (768x1269) "Maps merely constitute information which varies in accuracy from case to case of themselves, and by virtue solely of their existence, they cannot constitute a territorial title, that is, a document endowed by international law with intrinsic legal force for the purpose of establishing territorial rights." Burkina Faso/Republic of Mali (1986 I.C.J. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |