Zhiyuan Xu
Cited by
Cited by
Experience-driven networking: A deep reinforcement learning based approach
Z Xu, J Tang, J Meng, W Zhang, Y Wang, CH Liu, D Yang
IEEE INFOCOM 2018-IEEE conference on computer communications, 1871-1879, 2018
Spatiotemporal modeling and prediction in cellular networks: A big data enabled deep learning approach
J Wang, J Tang, Z Xu, Y Wang, G Xue, X Zhang, D Yang
IEEE INFOCOM 2017-IEEE conference on computer communications, 1-9, 2017
A hierarchical framework of cloud resource allocation and power management using deep reinforcement learning
N Liu, Z Li, J Xu, Z Xu, S Lin, Q Qiu, J Tang, Y Wang
2017 IEEE 37th international conference on distributed computing systems …, 2017
A deep reinforcement learning based framework for power-efficient resource allocation in cloud RANs
Z Xu, Y Wang, J Tang, J Wang, MC Gursoy
2017 IEEE International Conference on Communications (ICC), 1-6, 2017
Autocompress: An automatic dnn structured pruning framework for ultra-high compression rates
N Liu, X Ma, Z Xu, Y Wang, J Tang, J Ye
Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 4876-4883, 2020
Experience-driven congestion control: When multi-path TCP meets deep reinforcement learning
Z Xu, J Tang, C Yin, Y Wang, G Xue
IEEE Journal on Selected Areas in Communications 37 (6), 1325-1336, 2019
Learning-based energy-efficient data collection by unmanned vehicles in smart cities
B Zhang, CH Liu, J Tang, Z Xu, J Ma, W Wang
IEEE Transactions on Industrial Informatics 14 (4), 1666-1676, 2017
Deep reinforcement learning for dynamic treatment regimes on medical registry data
Y Liu, B Logan, N Liu, Z Xu, J Tang, Y Wang
2017 IEEE international conference on healthcare informatics (ICHI), 380-385, 2017
Model-free control for distributed stream data processing using deep reinforcement learning
T Li, Z Xu, J Tang, Y Wang
Proceedings of the VLDB Endowment, 2018, 2018
Adversarial meta-learning
C Yin, J Tang, Z Xu, Y Wang
arXiv preprint arXiv:1806.03316, 2018
Knowledge transfer in multi-task deep reinforcement learning for continuous control
Z Xu, K Wu, Z Che, J Tang, J Ye
Advances on Neural Information Processing Systems (NeurIPS) 2020, 2020
Learning the dynamic treatment regimes from medical registry data through deep Q-network
N Liu, Y Liu, B Logan, Z Xu, J Tang, Y Wang
Scientific reports 9 (1), 1495, 2019
Memory augmented deep recurrent neural network for video question answering
C Yin, J Tang, Z Xu, Y Wang
IEEE transactions on neural networks and learning systems 31 (9), 3159-3167, 2019
Rgb-depth fusion gan for indoor depth completion
H Wang, M Wang, Z Che, Z Xu, X Qiao, M Qi, F Feng, J Tang
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022
Bridging the gap between semantic segmentation and instance segmentation
C Yin, J Tang, T Yuan, Z Xu, Y Wang
IEEE Transactions on Multimedia 24, 4183-4196, 2021
Cadre: A cascade deep reinforcement learning framework for vision-based autonomous urban driving
Y Zhao, K Wu, Z Xu, Z Che, Q Lu, J Tang, CH Liu
Proceedings of the AAAI conference on artificial intelligence 36 (3), 3481-3489, 2022
An actor-critic-based transfer learning framework for experience-driven networking
Z Xu, D Yang, J Tang, Y Tang, T Yuan, Y Wang, G Xue
IEEE/ACM Transactions on Networking 29 (1), 360-371, 2020
Teach less, learn more: On the undistillable classes in knowledge distillation
Y Zhu, N Liu, Z Xu, X Liu, W Meng, L Wang, Z Ou, J Tang
Advances in Neural Information Processing Systems 35, 32011-32024, 2022
Scalekd: Distilling scale-aware knowledge in small object detector
Y Zhu, Q Zhou, N Liu, Z Xu, Z Ou, X Mou, J Tang
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023
Hierarchical graph attention network for few-shot visual-semantic learning
C Yin, K Wu, Z Che, B Jiang, Z Xu, J Tang
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021
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