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2020 – today
- 2024
[j8]Chen Hou
, Rongye Shi, Qilong Huang, Yifang Wang:
Energy Harvest of Multiple Smart Sensors With Real-Time Fault-Detection. IEEE Trans Autom. Sci. Eng. 21(2): 1592-1606 (2024)- [j7]Yanjun Pu, Fang Liu, Rongye Shi, Haitao Yuan, Ruibo Chen, Tianhao Peng, Wenjun Wu:
ELAKT: Enhancing Locality for Attentive Knowledge Tracing. ACM Trans. Inf. Syst. 42(4): 112:1-112:27 (2024) - [c13]Xin Yu, Rongye Shi, Pu Feng, Yongkai Tian, Simin Li, Shuhao Liao, Wenjun Wu:
Leveraging Partial Symmetry for Multi-Agent Reinforcement Learning. AAAI 2024: 17583-17590 - [c12]Yongkai Tian, Xin Yu, Yirong Qi, Li Wang, Pu Feng, Wenjun Wu, Rongye Shi, Jie Luo:
Exploiting Hierarchical Symmetry in Multi-Agent Reinforcement Learning. ECAI 2024: 2202-2209 - [c11]Xin Yu, Yongkai Tian, Li Wang, Pu Feng, Wenjun Wu, Rongye Shi:
AdaptAUG: Adaptive Data Augmentation Framework for Multi-Agent Reinforcement Learning. ICRA 2024: 10814-10820 - [i10]Xin Yu, Rongye Shi, Pu Feng, Yongkai Tian, Simin Li, Shuhao Liao, Wenjun Wu:
Leveraging Partial Symmetry for Multi-Agent Reinforcement Learning. CoRR abs/2401.00167 (2024) - [i9]Pu Feng, Junkang Liang, Size Wang, Xin Yu, Rongye Shi, Wenjun Wu:
Hierarchical Consensus-Based Multi-Agent Reinforcement Learning for Multi-Robot Cooperation Tasks. CoRR abs/2407.08164 (2024) - 2023
- [j6]Xuan Di, Rongye Shi, Zhaobin Mo, Yongjie Fu:
Physics-Informed Deep Learning for Traffic State Estimation: A Survey and the Outlook. Algorithms 16(6): 305 (2023) - [j5]Zhaobin Mo, Xuan Di, Rongye Shi:
Robust Data Sampling in Machine Learning: A Game-Theoretic Framework for Training and Validation Data Selection. Games 14(1): 13 (2023) - [c10]Xin Yu, Rongye Shi, Pu Feng, Yongkai Tian, Jie Luo, Wenjun Wu:
ESP: Exploiting Symmetry Prior for Multi-Agent Reinforcement Learning. ECAI 2023: 2946-2953 - [c9]Jiabin Lou, Wenjun Wu, Shuhao Liao, Rongye Shi:
Air-M: A Visual Reality Many-Agent Reinforcement Learning Platform for Large-Scale Aerial Unmanned System. IROS 2023: 5598-5605 - [i8]Xuan Di, Rongye Shi, Zhaobin Mo, Yongjie Fu:
Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook. CoRR abs/2303.02063 (2023) - [i7]Xin Yu, Rongye Shi, Pu Feng, Yongkai Tian, Jie Luo, Wenjun Wu:
ESP: Exploiting Symmetry Prior for Multi-Agent Reinforcement Learning. CoRR abs/2307.16186 (2023) - 2022
- [j4]Xiaolin Liu, Rongye Shi, Qianxin Hui, Susu Xu, Shuai Wang, Rui Na, Ying Sun, Wenbo Ding, Dezhi Zheng, Xinlei Chen:
TCACNet: Temporal and channel attention convolutional network for motor imagery classification of EEG-based BCI. Inf. Process. Manag. 59(5): 103001 (2022) - [j3]Rongye Shi, Zhaobin Mo, Kuang Huang, Xuan Di, Qiang Du:
A Physics-Informed Deep Learning Paradigm for Traffic State and Fundamental Diagram Estimation. IEEE Trans. Intell. Transp. Syst. 23(8): 11688-11698 (2022) - [j2]Zhiyong Yu, Huijuan Chang, Zhiwen Yu, Bin Guo, Rongye Shi:
Location Selection for Air Quality Monitoring With Consideration of Limited Budget and Estimation Error. IEEE Trans. Mob. Comput. 21(11): 4025-4037 (2022) - [c8]Chengzhao Yu, Ji Luo, Rongye Shi, Xinyu Liu, Fan Dang, Xinlei Chen:
ST-ICM: spatial-temporal inference calibration model for low cost fine-grained mobile sensing. MobiCom 2022: 910-912 - 2021
- [j1]Rongye Shi, Peter Steenkiste, Manuela M. Veloso:
Improving the On-Vehicle Experience of Passengers Through SC-M*: A Scalable Multi-Passenger Multi-Criteria Mobility Planner. IEEE Trans. Intell. Transp. Syst. 22(2): 1026-1040 (2021) - [c7]Rongye Shi, Zhaobin Mo, Xuan Di:
Physics-Informed Deep Learning for Traffic State Estimation: A Hybrid Paradigm Informed By Second-Order Traffic Models. AAAI 2021: 540-547 - [c6]Cristian Axenie, Rongye Shi, Daniele Foroni, Alexander Wieder, Mohamad Al Hajj Hassan, Paolo Sottovia, Margherita Grossi, Stefano Bortoli, Götz Brasche:
TRAMESINO: Traffic Memory System for Intelligent Optimization of Road Traffic Control. AALTD@ECML/PKDD 2021: 88-103 - [c5]Cristian Axenie, Rongye Shi, Daniele Foroni, Alexander Wieder, Mohamad Al Hajj Hassan, Paolo Sottovia, Margherita Grossi, Stefano Bortoli, Götz Brasche:
OBELISC: Oscillator-Based Modelling and Control Using Efficient Neural Learning for Intelligent Road Traffic Signal Calculation. ECML/PKDD (4) 2021: 437-452 - [i6]Rongye Shi, Zhaobin Mo, Kuang Huang, Xuan Di, Qiang Du:
Physics-Informed Deep Learning for Traffic State Estimation. CoRR abs/2101.06580 (2021) - [i5]Rongye Shi, Zhaobin Mo, Kuang Huang, Xuan Di, Qiang Du:
A Physics-Informed Deep Learning Paradigm for Traffic State Estimation and Fundamental Diagram Discovery. CoRR abs/2106.03142 (2021) - 2020
- [i4]Zhicheng Li, Zhihao Gu, Xuan Di, Rongye Shi:
An LSTM-Based Autonomous Driving Model Using Waymo Open Dataset. CoRR abs/2002.05878 (2020) - [i3]Xuan Di, Rongye Shi:
A Survey on Autonomous Vehicle Control in the Era of Mixed-Autonomy: From Physics-Based to AI-Guided Driving Policy Learning. CoRR abs/2007.05156 (2020) - [i2]Zhaobin Mo, Xuan Di, Rongye Shi:
A Physics-Informed Deep Learning Paradigm for Car-Following Models. CoRR abs/2012.13376 (2020)
2010 – 2019
- 2018
- [c4]Rongye Shi, Peter Steenkiste, Manuela Veloso:
Generating Synthetic Passenger Data through Joint Traffic-Passenger Modeling and Simulation. ITSC 2018: 3397-3402 - [i1]Ruizhou Ding, Zeye Liu, Rongye Shi, Diana Marculescu, R. D. (Shawn) Blanton:
LightNN: Filling the Gap between Conventional Deep Neural Networks and Binarized Networks. CoRR abs/1802.02178 (2018) - 2017
- [c3]Rongye Shi, Peter Steenkiste, Manuela M. Veloso:
Second-Order Destination Inference using Semi-Supervised Self-Training for Entry-Only Passenger Data. BDCAT 2017: 255-264 - [c2]Ruizhou Ding, Zeye Liu, Rongye Shi, Diana Marculescu, R. D. (Shawn) Blanton:
LightNN: Filling the Gap between Conventional Deep Neural Networks and Binarized Networks. ACM Great Lakes Symposium on VLSI 2017: 35-40 - 2016
- [c1]Rongye Shi, Thomas C. Jackson, Brian Swenson, Soummya Kar, Lawrence T. Pileggi:
On the design of phase locked loop oscillatory neural networks: Mitigation of transmission delay effects. IJCNN 2016: 2039-2046
Coauthor Index
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