| Deep anomaly detection with self-supervised learning and adversarial training X Zhang, J Mu, X Zhang, H Liu, L Zong, Y Li Pattern Recognition, 108234, 2021 | 76 | 2021 |
| End-To-End Deep Multimodal Clustering X Zhang, J Mu, L Zong, X Yang 2020 IEEE International Conference on Multimedia and Expo (ICME), 1-6, 2020 | 12 | 2020 |
| Deep multimodal clustering with cross reconstruction X Zhang, X Tang, L Zong, X Liu, J Mu Advances in Knowledge Discovery and Data Mining 12084, 305, 2020 | 11 | 2020 |
| MOCOLNet: A Momentum Contrastive Learning Network for Multimodal Aspect-Level Sentiment Analysis J Mu, F Nie, W Wang, J Xu, J Zhang, H Liu IEEE Transactions on Knowledge and Data Engineering, 2023 | 10 | 2023 |
| Deep neural network for text anomaly detection in SIoT J Mu, X Zhang, Y Li, J Guo Computer Communications, 2021 | 10 | 2021 |
| Graphnet: Graph Clustering with Deep Neural Networks X Zhang, J Mu, H Liu, X Zhang ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 7 | 2021 |
| Graph Clustering With Graph Capsule Network X Zhang, J Mu, H Liu, X Zhang, L Zong, G Wang Neural Computation 34 (5), 1256-1287, 2022 | 3 | 2022 |
| BCRNet: Bidirectional contrastive representation network for deep multimodal learning of exercise representations in online education systems J Mu, X Zhang, Y Du, Y Hu, H Liu, J Xu Neurocomputing 548, 126409, 2023 | 1 | 2023 |
| Maintaining Consistency with Constraints: A Constrained Deep Clustering Method Y Cui, X Zhang, L Zong, J Mu Pacific-Asia Conference on Knowledge Discovery and Data Mining, 219-230, 2021 | 1 | 2021 |
| Self-supervised Graph Representation Learning with Variational Inference Z Liao, W Liang, H Liu, J Mu, X Zhang Pacific-Asia Conference on Knowledge Discovery and Data Mining, 116-127, 2021 | 1 | 2021 |
| Continual Learning with Class-Level Minimally Interfered Update G Wang, X Zhang, H Liu, X Zhang, J Mu, W Yang, L Zong ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and …, 2024 | | 2024 |
Han LiuAssociate Professor, Dalian University of TechnologyΗ διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα dlut.edu.cn