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多模态人工智能系统... [11]

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会议论文 [8]

期刊论文 [2]

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2016 [11]

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	An efficient realization of deep learning for traffic data imputation 期刊论文 TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2016, 卷号: 72, 页码: 168-181 作者: Duan, Yanjie; Lv, Yisheng; Liu, Yu-Liang; Wang, Fei-Yue Adobe PDF(824Kb) \| 收藏 \| 浏览/下载：553/270 \| 提交时间：2017/02/14 Traffic Data Imputation Deep Learning Missing Data
	Autonomous Vehicles Testing Methods Review 会议论文 , Windsor Oceanico Hotel, Rio de Janeiro, Brazil, November 1-4, 2016 作者: WuLing Huang; Kunfeng Wang; Yisheng Lv; FengHua Zhu 浏览 \| Adobe PDF(506Kb) \| 收藏 \| 浏览/下载：1316/968 \| 提交时间：2018/01/08
	Analysis and forecasting of urban traffic condition based on categorical data 会议论文 , Beijing, China, 10-12 July 2016 作者: Chen, Yuanyuan; Lv, Yisheng 浏览 \| Adobe PDF(572Kb) \| 收藏 \| 浏览/下载：208/61 \| 提交时间：2018/01/08
	Paragraph vector based retrieval model for similar cases recommendation 会议论文 , Guilin, China, 12-15 June 2016 作者: Zhao, Yifei; Wang, Jing; Wang, Fei-Yue; Shi, Xiaobo; Lv, Yisheng 收藏 \| 浏览/下载：145/0 \| 提交时间：2018/01/08
	Discharge model and control strategy for E-bicycle mixed intersections 会议论文 , Guilin, China, 12-15 June 2016 作者: Liu, Yu-liang; Lv, Yisheng 收藏 \| 浏览/下载：117/0 \| 提交时间：2018/01/08
	Traffic Signal Timing via Deep Reinforcement Learning 期刊论文 IEEE/CAA Journal of Automatica Sinica, 2016, 期号: 3, 页码: 247-254 作者: Li Li; Lv YS(吕宜生); Fei-Yue Wang Adobe PDF(509Kb) \| 收藏 \| 浏览/下载：70/33 \| 提交时间：2022/04/08 Traffic control , reinforcement learning , deep learning , deep reinforcement learning
	Long short-term memory model for traffic congestion prediction with online open data 会议论文 , Rio de Janeiro, Brazil, 1-4 Nov. 2016 作者: Chen, Yuanyuan; Lv, Yisheng; Li, Zhenjiang; Wang, Fei-Yue 浏览 \| Adobe PDF(575Kb) \| 收藏 \| 浏览/下载：385/181 \| 提交时间：2018/01/08
	Travel Time Prediction with LSTM Neural Network 会议论文 , Rio de Janeiro, Brazil, 2016年11月1-4日作者: Duan YJ(段艳杰); Lv Yisheng; Wang Fei-Yue 浏览 \| Adobe PDF(189Kb) \| 收藏 \| 浏览/下载：1246/834 \| 提交时间：2017/05/04
	Performance Evaluation of the Deep Learning Approach for Traffic Flow Prediction at Different Times 会议论文 , Beijing, P.R. China, 2016年7月10-12日作者: Duan YJ(段艳杰); Lv Yisheng; Wang Fei-Yue 浏览 \| Adobe PDF(154Kb) \| 收藏 \| 浏览/下载：274/93 \| 提交时间：2017/05/04
	Parallel public transportation system and its application in evaluating evacuation plans for large-scale activities 专著章节/文集论文出自: Big Data and Smart Service System, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, 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