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BAI-Net: Individualized Anatomical Cerebral Cartography Using Graph Neural Network 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 页码: 1-12
作者:  Ma L(马亮);  Zhang Y(张瑜);  Zhang HT(张瀚天);  Cheng LQ(程禄祺);  Yang ZY(杨正宜);  Lu YH(陆玉恒);  Shi WY(时维阳);  Li W(李雯);  Zhuo JJ(卓俊杰);  Wang JJ(王娇健);  Fan LZ(樊令仲);  Jiang TZ(蒋田仔)
Adobe PDF(16100Kb)  |  收藏  |  浏览/下载：210/44  |  提交时间：2023/01/04
anatomical connectivity fingerprints  heritability  individualized brain atlas  graph neural network  topography variability  
PIRNet: Personality-Enhanced Iterative Refinement Network for Emotion Recognition in Conversation 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 页码: 12
作者:  Lian, Zheng;  Liu, Bin;  Tao, Jianhua
Adobe PDF(2013Kb)  |  收藏  |  浏览/下载：456/14  |  提交时间：2022/09/19
Emotion recognition  Iterative methods  Context modeling  Psychology  Oral communication  Logic gates  Learning systems  Contextual information  emotion recognition in conversation (ERC)  iterative method  Personality-enhanced Iterative Refinement Network (PIRNet)  personality influence  
Structured Neural Decoding With Multitask Transfer Learning of Deep Neural Network Representations 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 卷号: 33, 期号: 2, 页码: 600-614
作者:  Du, Changde;  Du, Changying;  Huang, Lijie;  Wang, Haibao;  He, Huiguang
Adobe PDF(8742Kb)  |  收藏  |  浏览/下载：460/171  |  提交时间：2022/03/17
Decoding  Image reconstruction  Functional magnetic resonance imaging  Visualization  Task analysis  Brain  Correlation  Deep neural network (DNN)  functional magnetic resonance imaging (fMRI)  image reconstruction  multioutput regression  neural decoding  
Boost 3-D Object Detection via Point Clouds Segmentation and Fused 3-D GIoU-L-1 Loss 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 卷号: 33, 期号: 2, 页码: 762-773
作者:  Chen, Yaran;  Li, Haoran;  Gao, Ruiyuan;  Zhao, Dongbin
Adobe PDF(2082Kb)  |  收藏  |  浏览/下载：314/77  |  提交时间：2022/03/17
3-D object detection  generalized Intersection of Union (GIoU) loss  segmentation