中文版 | English

在结果中检索

CASIA OpenIR

浏览/检索结果: 共7条，第1-7条 帮助

限定条件                        
已选(0)清除 条数/页：   排序方式：
A Review of Computational Intelligence for StarCraft AI 会议论文
, Bangalore, India, 18-21 Nov. 2018
作者:  Tang, Zhentao;  Shao, Kun;  Zhu, Yuanheng;  Li, Dong;  Zhao, Dongbin;  Huang, Tingwen
浏览  |  Adobe PDF(131Kb)  |  收藏  |  浏览/下载：487/224  |  提交时间：2019/04/25
CSAN: Contextual Self-Attention Network for User Sequential Recommendation 会议论文
, Seoul, Republic of Korea, October 22-26, 2018
作者:  Xiaowen Huang;  Shengsheng Qian;  Quan Fang;  Jitao Sang;  Changsheng Xu
浏览  |  Adobe PDF(3198Kb)  |  收藏  |  浏览/下载：380/161  |  提交时间：2019/09/26
Learning 3D Keypoint Descriptors for Non-Rigid Shape Matching 会议论文
, Munich, Germany, September 8 – 14, 2018
作者:  Wang, Hanyu;  Guo, Jianwei;  Yan, Dong-Ming;  Quan, Weize;  Zhang, Xiaopeng
浏览  |  Adobe PDF(4072Kb)  |  收藏  |  浏览/下载：461/113  |  提交时间：2018/10/01
Economic Issues in Bitcoin Minning and Blockchain Research 会议论文
Proceedings of the 29th IEEE Intelligent Vehicles Symposium, Changshu, China, June 26-29, 2018
作者:  Qin, Rui;  Yuan, Yong;  Wang, Shuai;  Wang, Fei-Yue
浏览  |  Adobe PDF(885Kb)  |  收藏  |  浏览/下载：737/341  |  提交时间：2018/09/20
Blockchain  Bitcoin Mining  Mining Pool  Blockchain Network  Economic Issues  
An overview of smart contract: Architecture, applications, and future trends 会议论文
, Changshu, China, Jun. 26-29, 2018.
作者:  Shuai Wang;  Yong Yuan;  Xiao Wang;  Juanjuan Li;  Rui Qin;  Fei-Yue Wang
浏览  |  Adobe PDF(386Kb)  |  收藏  |  浏览/下载：1488/1059  |  提交时间：2018/10/10
Generative Adversarial Networks for Parallel Vision 会议论文
, Jinan, China, 20-22 Oct. 2017
作者:  Kunfeng Wang;  Xuan Li;  Lan Yan;  Fei-Yue Wang
浏览  |  Adobe PDF(1468Kb)  |  收藏  |  浏览/下载：293/94  |  提交时间：2018/01/08
A Partition and Interaction Combined Model for Social Event Popularity Prediction 会议论文
, Miami, FL, USA, 2018.11.08-2018.11.10
作者:  Guandan Chen;  Qingchao Kong;  Wenji Mao;  Daniel Zeng
浏览  |  Adobe PDF(1095Kb)  |  收藏  |  浏览/下载：342/110  |  提交时间：2018/10/10
Popularity Prediction  Information Cascade  Reinforcement Learning