Ph.D., Associate Professor, |
[2024/05] "Socialized learning: making each other better through multi-agent collaboration" has been accepted by International Conference on Machine Learning (ICML).
[2024/04] "Integrated heterogeneous graph attention network for incomplete multi-modal clustering" has been published on International Journal of Computer Vision (IJCV).
[2024/02] "Multi-view deep subspace clustering networks" has been published on IEEE Transactions on Cybernetics (TCYB).
[2024/01] "Exploring diverse representations for open set recognition" has been published on Proceedings of the AAAI Conference on Artificial Intelligence (AAAI).
[2024/01] "Every node is different: dynamically fusing self-supervised tasks for attributed graph clustering" has been published on Proceedings of the AAAI Conference on Artificial Intelligence (AAAI).
[2024/01] "Dynamic sub-graph distillation for robust semi-supervised continual learning" has been published on Proceedings of the AAAI Conference on Artificial Intelligence (AAAI).
[2023/12] "Few-shot learning with multi-granularity knowledge fusion and decision-making" has been published on IEEE Transactions on Big Data (TBD).
[2023/12] "Industrial big data analytical system in industrial cyber-physical systems based on coarse-to-fine deep network" has been published on IEEE Transactions on Industrial Cyber-Physical Systems (TICPS).
[2023/07] "Coarse-to-fine: progressive knowledge transfer based multi-task convolutional neural network for intelligent large-scale fault diagnosis" has been published by IEEE Transactions on Neural Networks and Learning Systems (TNNLS).
[2023/06] Our team has won the championship of CVPR 2023 Open World Object Discovery Challenge.
[2023/06] Our team has won the runner up of CVPR 2023 Continual Learning Challenge.
[2023/04] "Class-specific semantic reconstruction for open set recognition" has been published on IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).
[2023/04] "Multi-granularity regularized re-balancing for class incremental learning" has been published on IEEE Transactions on Knowledge and Data Engineering (TKDE).
Wang Yu (王煜) is currently an associate professor at Tianjin University (TJU) and Tianjin Key Laboratory of Machine Learning led by Prof. Hu Qinghua (胡清华). He received BA, ME, and Ph.D degrees from Tianjin University. His researches lie in data mining and machine learning in artificial intelligence, including multi-granularity classification, open-set recognition, incremental learning, and their applications to image classification and fault diagnosis of complex systems. He has published many papers in highly regarded journals and conferences, such as IEEE TPAMI, IEEE TKDE, IEEE TNNLS, IEEE TCYB, etc.
Call for graduate and undergraduate students! Hope you are hardworking, determined, and have basic programming skills and mathematics knowledge. You can receive detailed supervision on research and would have a promising career. Feel free to contact me via email.
[TPAMI CCF A] |
[IJCV CCF A] |
[AAAI CCF A] |
[AAAI CCF A] |
[TKDE CCF A] |
[TCYB CAS-JCR Q1 Top] |
[TNNLS CAS-JCR (中科院分区) Q1 Top] |
[AAAI CCF A] |
[TNNLS CAS-JCR Q1 Top] |
[软件学报 CCF A] |
[TMM CAS-JCR Q1 Top] |
[TFS CAS-JCR Q1 Top] |
He serves as a reviewer of:
IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
IEEE Transactions on Image Processing (TIP)
IEEE Transactions on Cybernetics (TCYB)
IEEE Internet of Things Journal (IoTJ)
IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
IEEE International Conference on Computer Vision (ICCV)
etc