Jian Tang
Jian Tang
Professor, IEEE Fellow and ACM Distinguished Member, Department of EECS, Syracuse University
Adresă de e-mail confirmată pe syr.edu - Pagina de pornire
Citat de
Citat de
Crowdsourcing to smartphones: Incentive mechanism design for mobile phone sensing
D Yang, G Xue, X Fang, J Tang
ACM MobiCom 2012: Proceedings of the 18th annual international conference on …, 2012
Interference-aware topology control and QoS routing in multi-channel wireless mesh networks
J Tang, G Xue, W Zhang
ACM MobiHoc 2005: Proceedings of the 6th ACM international symposium on …, 2005
Energy-efficient UAV control for effective and fair communication coverage: A deep reinforcement learning approach
CH Liu, Z Chen, J Tang, J Xu, C Piao
IEEE Journal on Selected Areas in Communications 36 (9), 2059-2070, 2018
A systematic dnn weight pruning framework using alternating direction method of multipliers
T Zhang, S Ye, K Zhang, J Tang, W Wen, M Fardad, Y Wang
Proceedings of the European conference on computer vision (ECCV), 184-199, 2018
Incentive mechanisms for crowdsensing: Crowdsourcing with smartphones
D Yang, G Xue, X Fang, J Tang
IEEE/ACM transactions on networking 24 (3), 1732-1744, 2015
Relay node placement in large scale wireless sensor networks
J Tang, B Hao, A Sen
Computer Communications Journal 29 (4), 490-501, 2006
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
C ir CNN: accelerating and compressing deep neural networks using block-circulant weight matrices
C Ding, S Liao, Y Wang, Z Li, N Liu, Y Zhuo, C Wang, X Qian, Y Bai, ...
IEEE/ACM MICRO 2017: Proceedings of the 50th Annual IEEE/ACM International …, 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
Optimizing electric vehicle charging: A customer's perspective
C Jin, J Tang, P Ghosh
IEEE Transactions on vehicular technology 62 (7), 2919-2927, 2013
Optimizing electric vehicle charging with energy storage in the electricity market
C Jin, J Tang, P Ghosh
IEEE Transactions on Smart Grid 4 (1), 311-320, 2013
T-storm: Traffic-aware online scheduling in storm
J Xu, Z Chen, J Tang, S Su
IEEE ICDCS 2014: 34th IEEE International Conference on Distributed Computing …, 2014
Sensing as a service: Challenges, solutions and future directions
X Sheng, J Tang, X Xiao, G Xue
IEEE Sensors Journal 13 (10), 3733-3741, 2013
Distributed energy-efficient multi-UAV navigation for long-term communication coverage by deep reinforcement learning
CH Liu, X Ma, X Gao, J Tang
IEEE Transactions on Mobile Computing 19 (6), 1274-1285, 2019
Constrained relay node placement in wireless sensor networks: Formulation and approximations
S Misra, SD Hong, G Xue, J Tang
IEEE/ACM Transactions on networking 18 (2), 434-447, 2009
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
Truthful incentive mechanisms for crowdsourcing
X Zhang, G Xue, R Yu, D Yang, J Tang
IEEE INFOCOM 2015: IEEE Conference on Computer Communications, 2830-2838, 2015
Fault-tolerant relay node placement in wireless sensor networks: formulation and approximation
B Hao, J Tang, G Xue
IEEE HPSR 2004: IEEE Workshop on High Performance Switching and Routing, 246-250, 2004
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