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An Empirical Study on Google Research Football Multi-agent Scenarios 期刊论文
Machine Intelligence Research, 2024, 卷号: 21, 期号: 3, 页码: 549-570
Authors:  Yan Song;  He Jiang;  Zheng Tian;  Haifeng Zhang;  Yingping Zhang;  Jiangcheng Zhu;  Zonghong Dai;  Weinan Zhang;  Jun Wang
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Multi-agent reinforcement learning (RL), distributed RL system, population-based training, reward shaping, game theory  
工业铸件缺陷无损检测技术的应用进展与展望 期刊论文
自动化学报, 2022, 卷号: 48, 期号: 4, 页码: 935-956
Authors:  张辉;  张邹铨;  陈煜嵘;  吴天月;  钟杭;  王耀南
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铸造缺陷  无损检测  X射线探测  神经网络  
基于分步子空间映射的无标记膈肌运动预测算法 期刊论文
自动化学报, 2022, 卷号: 48, 期号: 5, 页码: 1327-1342
Authors:  余航;  李晨阳;  余绍德;  冯冬竹;  许录平
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呼吸运动  胸腹部表面  膈肌  子空间映射  回归模型  4D CT  
基于多模型融合的肺部CT新冠肺炎病灶区域自动分割 期刊论文
自动化学报, 2023, 卷号: 49, 期号: 2, 页码: 317-328
Authors:  史天意;  程枫;  李震;  郑传胜;  许永超;  白翔
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新冠肺炎  计算机断层扫描影像分割  深度学习  泛化性能  
基于双模型交互学习的半监督医学图像分割 期刊论文
自动化学报, 2023, 卷号: 49, 期号: 4, 页码: 805-819
Authors:  方超伟;  李雪;  李钟毓;  焦李成;  张鼎文
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半监督学习  医学图像分割  双模型交互学习  平均教师  
X射线工业CT成像过程复杂伪影抑制方法综述 期刊论文
自动化学报, 2023, 卷号: 49, 期号: 4, 页码: 687-704
Authors:  杨富强;  杨瑶;  李志翔;  黄魁东
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工业CT  伪影抑制  深度学习  智能检测  
基于静−动态特性协同感知的复杂工业过程运行状态评价 期刊论文
自动化学报, 2023, 卷号: 49, 期号: 8, 页码: 1621-1634
Authors:  褚菲;  许杨;  尚超;  王福利;  高福荣;  马小平
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复杂工业过程  运行状态评价  静-动态特性协同  慢特征分析  稀疏学习  
A Simple yet Effective Framework for Active Learning to Rank 期刊论文
Machine Intelligence Research, 2024, 卷号: 21, 期号: 1, 页码: 169-183
Authors:  Qingzhong Wang;  Haifang Li;  Haoyi Xiong;  Wen Wang;  Jiang Bian;  Yu Lu;  Shuaiqiang Wang;  Zhicong Cheng;  Dejing Dou;  Dawei Yin
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Search, information retrieval, learning to rank, active learning, query by committee  
State of the Art on Deep Learning-enhanced Rendering Methods 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 6, 页码: 799-821
Authors:  Qi Wang;  Zhihua Zhong;  Yuchi Huo;  Hujun Bao;  Rui Wang
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Neural rendering, computer graphics, scene representation, rendering, post-processing  
Machine Learning in Lung Cancer Radiomics 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 6, 页码: 753-782
Authors:  Jiaqi Li;  Zhuofeng Li;  Lei Wei;  Xuegong Zhang
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Machine learning, lung cancer, radiomics, medical image, clinical application