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[JSAC]. C. H. Liu*, Z. Chen, J. Tang, J. Xu, C. Piao, "Energy-Efficient UAV Control for Effective and Fair Communication Coverage: A Deep Reinforcement Learning Approach," IEEE Journal of Selected Areas in Communications, Volume:3, Issue:9, Page(s): 2059-2070, 2018. 高被引论文
[JSAC]. C. H. Liu*, Z. Chen, Y. Zhan, "Energy-Efficient Distributed Mobile Crowd Sensing: A Deep Learning Approach," IEEE Journal of Selected Areas in Communications, Volume: 37, Issue: 6, Page(s): 1262 – 1276, June 2019.
[TKDE]. C. H. Liu*, J. Xu, J. Tang and J. Crowcroft, "Social-aware Sequential Modeling of User Interests: A Deep Learning Approach," IEEE Transactions on Knowledge and Data Engineering, Volume: 31, Issue: 11, Page(s): 2200 – 2212, Nov. 1 2019.
[TKDE]. C. H. Liu*, Y. Wang, C. Piao, Z. Dai, Y. Yuan, G. Wang, D. Wu, "Time-Aware Location Prediction by Convolutional Area-of-Interest Modeling and Memory-Augmented Attentive LSTM," IEEE Transactions on Knowledge and Data Engineering, DOI: 10.1109/TKDE.2020.3005735, June 2020.
[TMC]. C. H. Liu, X. Ma, X. Gao and J. Tang*, "Distributed Energy-Efficient Multi-UAV Navigation for Long-Term Communication Coverage by Deep Reinforcement Learning," IEEE Transactions on Mobile Computing, Volume: 19, Issue:6, Page(s): 1274-1285, JUNE 2020
[TMC]. C. H. Liu*, Z. Dai, Y. Zhao, J. Crowcroft, D. Wu and K. K. Leung, "Distributed and Energy-Efficient Mobile Crowdsensing with Charging Stations by Deep Reinforcement Learning," IEEE Transactions on Mobile Computing, Volume: 20, Issue: 1, Page(s): 130-146, January 2021.
[TPAMI]. S. Li, C. H. Liu*, Q. Lin, Q. Wen, L. Su, G. Huang, Z. Ding, "Deep Residual Correction Network for Partial Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume:43, Issue: 7, Page(s): 2329-2344, July 2021. 高被引论文
[TPAMI]. S. Li, B. Xie, Q. Lin, C. H. Liu, G. Huang, and G. Wang,“Generalized Domain Conditioned Adaptation Network,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, DOI: 10.1109/TPAMI.2021.3062644, 01 March 2021.
[TPAMI]. B. Xie, S. Li*, M. Li, C. H. Liu, G. Huang, and G. Wang,“SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation,”IEEE Transactions on Pattern Analysis and Machine Intelligence, Jan. 2023.
[TVCG]. G. Li, R. Li, Z. Wang, C. H. Liu*, M. Lu, and G. Wang,“HiTailor: Interactive Transformation and Visualization for Hierarchical Tabular Data,”IEEE Transactions on Visualization and Computer Graphics, Volume: 29, Issue: 1, Page(s): 139-148, Jan. 2023.
[TC]. R. Han, C. H. Liu*, S. Li, S. Wen, and X. Liu,“Accelerating Deep Learning Systems via Critical Set Identification and Model Compression,” IEEE Transactions on Computers, Volume: 69, Issue: 7, Page(s): 1059-1070, 1 July 2020.
[KDD]. H. Wang, C. H. Liu*, Z. Dai, J. Tang, G. Wang,“Energy-Efficient 3D Vehicular Crowdsourcing for Disaster Response by Distributed Deep Reinforcement Learning,”in ACM SIGKDD 2021 , virtual, August 2021, Page(s): 3679–3687 (Best Paper Award - Runner Up).
[MOBICOM]. R. Han, Q. Zhang, C. H. Liu*, G. Wang, J. Tang, L. Y. Chen,“LegoDNN: Block-grained Scaling of Deep Neural Networks for Mobile Vision,”in ACM MOBICOM 2021 , virtual, October 2021, Page(s): 406–419. (Best Community Paper Award - Runner Up).
[NeurIPS]. F. Lv, J. Liang, K. Gong, S. Li*, C. H. Liu, H. Li, D. Liu, G. Wang,“Pareto Domain Adaptation,” in NeurIPS 2021 , virtual, Dec. 6-14, 2021.
[ICLR]. M. Xie, S. Li*, R. Zhang, C. H. Liu,“Dirichlet-based Uncertainty Calibration for Active Domain Adaptation,” in ICLR 2023 , virtual, May 1-5, 2023.
[INFOCOM]. C. H. Liu, Z. Dai, H. Yang, J. Tang, “Multi-Task-Oriented Vehicular Crowdsensing: A Deep Learning Approach,”in IEEE INFOCOM 2020 , virtual, 6-9 July, Page(s): 1123-1132.
[INFOCOM]. C. H. Liu, C. Piao, J. Tang, “Energy-Efficient UAV Crowdsensing with Multiple Charging Stations by Deep Learning,”in IEEE INFOCOM 2020 , virtual, 6-9 July, Page(s): 199-208.
[INFOCOM]. Z. Dai, H. Wang, C. H. Liu*, R. Han, J. Tang, G. Wang,“Mobile Crowdsensing for Data Freshness: A Deep Reinforcement Learning Approach,”in IEEE INFOCOM 2021 , virtual, 10-13 May, 2021, Page(s): 1-10.
[INFOCOM]. Z. Dai, C. H. Liu*, Y. Ye, R. Han, Y. Yuan, G. Wang, J. Tang,“AoI-minimal UAV Crowdsensing by Model-based Graph Convolutional Reinforcement Learning,”in IEEE INFOCOM 2022 , Virtual, 2-5 May, 2022.
[ICDE]. C. H. Liu, Y. Zhao, Y. Yuan, G. Wang, D. Wu, K. K. Leung,“Curiosity Driven Energy-Efficient Worker Scheduling in Vehicular Crowdsourcing: A Deep Reinforcement Learning Approach,”in IEEE ICDE 2020 , Dallas, USA, April 2020, Page(s):25-36.
[ICDE]. C. H. Liu, C. Piao, X. Ma, Y. Yuan, J. Tang, G. Wang,“Modeling Citywide Crowd Flows using Attentive Convolutional LSTM,”in IEEE ICDE 2021 , virtual, April 19-23 2021, Page(s):217-228.
[ICDE]. Y. Wang, C. H. Liu*, C. Piao, Y. Yuan, R. Han, G. Wang,“Human-Drone Collaborative Spatial Crowdsourcing by Memory-Augmented and Multi-Agent Deep Reinforcement Learning,”in IEEE ICDE 2022 , virtual, May 9-12 2022.
[ICDE]. Y. Ye, C. H. Liu, Z. Dai, J. Zhao*, Y. Yuan, G. Wang, J. Tang,“Exploring both Individuality and Cooperation for Air-Ground Spatial Crowdsourcing by Multi-Agent Deep Reinforcement Learning,” in IEEE ICDE 2023 , virtual, 2023.
[CVPR]. S. Li, M. Xie, K. Gong, C. H. Liu*, Y. Wang, W. Li, “Transferable Semantic Data Augmentation for Domain Adaptation,” in IEEE CVPR 2021 , virtual, June 19-25, 2021, Page(s):11516-11525. (oral presentation)
[AAAI]. Y. Zhao, K. Wu, Z. Xu, Z. Che, Q. Lu, J. Tang, C. H. Liu, “A Cascade Deep Reinforcement Learning Framework for Vision-based Autonomous Urban Driving,” AAAI 2022 , Virtual, 22 Feb-1 March, 2022. (oral presentation)
代表性论著和教材
刘驰（主编）；韩锐、赵健鑫、马建（副主编），《物联网技术概论》第三版，机械工业出版社，2021年11月 北京理工大学“十四五”规划教材、北京理工大学精品教材
刘驰、王占健、马晓鑫、戴子彭等（编著），《深度强化学习：学术前沿与实践应用》，机械工业出版社，2020年4月
刘驰（主编）、符积高、徐闻春（编著）,《Spark:原理、机制及应用》，机械工业出版社，2016年3月
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