Artículos de revistas sobre el tema "Graph and Multi-view Memory Attention"
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Ai, Bing, Yibing Wang, Liang Ji, Jia Yi, Ting Wang, Wentao Liu y Hui Zhou. "A graph neural network fused with multi-head attention for text classification". Journal of Physics: Conference Series 2132, n.º 1 (1 de diciembre de 2021): 012032. http://dx.doi.org/10.1088/1742-6596/2132/1/012032.
Texto completoLiu, Di, Hui Xu, Jianzhong Wang, Yinghua Lu, Jun Kong y Miao Qi. "Adaptive Attention Memory Graph Convolutional Networks for Skeleton-Based Action Recognition". Sensors 21, n.º 20 (12 de octubre de 2021): 6761. http://dx.doi.org/10.3390/s21206761.
Texto completoFeng, Aosong, Irene Li, Yuang Jiang y Rex Ying. "Diffuser: Efficient Transformers with Multi-Hop Attention Diffusion for Long Sequences". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 11 (26 de junio de 2023): 12772–80. http://dx.doi.org/10.1609/aaai.v37i11.26502.
Texto completoLi, Mingxiao y Marie-Francine Moens. "Dynamic Key-Value Memory Enhanced Multi-Step Graph Reasoning for Knowledge-Based Visual Question Answering". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 10 (28 de junio de 2022): 10983–92. http://dx.doi.org/10.1609/aaai.v36i10.21346.
Texto completoJung, Tae-Won, Chi-Seo Jeong, In-Seon Kim, Min-Su Yu, Soon-Chul Kwon y Kye-Dong Jung. "Graph Convolutional Network for 3D Object Pose Estimation in a Point Cloud". Sensors 22, n.º 21 (25 de octubre de 2022): 8166. http://dx.doi.org/10.3390/s22218166.
Texto completoCui, Wei, Fei Wang, Xin He, Dongyou Zhang, Xuxiang Xu, Meng Yao, Ziwei Wang y Jiejun Huang. "Multi-Scale Semantic Segmentation and Spatial Relationship Recognition of Remote Sensing Images Based on an Attention Model". Remote Sensing 11, n.º 9 (2 de mayo de 2019): 1044. http://dx.doi.org/10.3390/rs11091044.
Texto completoHou, Miaomiao, Xiaofeng Hu, Jitao Cai, Xinge Han y Shuaiqi Yuan. "An Integrated Graph Model for Spatial–Temporal Urban Crime Prediction Based on Attention Mechanism". ISPRS International Journal of Geo-Information 11, n.º 5 (30 de abril de 2022): 294. http://dx.doi.org/10.3390/ijgi11050294.
Texto completoMi, Chunlei, Shifen Cheng y Feng Lu. "Predicting Taxi-Calling Demands Using Multi-Feature and Residual Attention Graph Convolutional Long Short-Term Memory Networks". ISPRS International Journal of Geo-Information 11, n.º 3 (9 de marzo de 2022): 185. http://dx.doi.org/10.3390/ijgi11030185.
Texto completoKarimanzira, Divas, Linda Ritzau y Katharina Emde. "Catchment Area Multi-Streamflow Multiple Hours Ahead Forecast Based on Deep Learning". Transactions on Machine Learning and Artificial Intelligence 10, n.º 5 (29 de septiembre de 2022): 15–29. http://dx.doi.org/10.14738/tmlai.105.13049.
Texto completoWang, Changhai, Jiaxi Ren y Hui Liang. "MSGraph: Modeling multi-scale K-line sequences with graph attention network for profitable indices recommendation". Electronic Research Archive 31, n.º 5 (2023): 2626–50. http://dx.doi.org/10.3934/era.2023133.
Texto completoMa, Xinwei, Yurui Yin, Yuchuan Jin, Mingjia He y Minqing Zhu. "Short-Term Prediction of Bike-Sharing Demand Using Multi-Source Data: A Spatial-Temporal Graph Attentional LSTM Approach". Applied Sciences 12, n.º 3 (23 de enero de 2022): 1161. http://dx.doi.org/10.3390/app12031161.
Texto completoLin, Chun, Yijia Xu, Yong Fang y Zhonglin Liu. "VulEye: A Novel Graph Neural Network Vulnerability Detection Approach for PHP Application". Applied Sciences 13, n.º 2 (6 de enero de 2023): 825. http://dx.doi.org/10.3390/app13020825.
Texto completoLiu, Daizong, Xiaoye Qu, Xing Di, Yu Cheng, Zichuan Xu y Pan Zhou. "Memory-Guided Semantic Learning Network for Temporal Sentence Grounding". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 2 (28 de junio de 2022): 1665–73. http://dx.doi.org/10.1609/aaai.v36i2.20058.
Texto completoWu, Jie, Ian G. Harris y Hongzhi Zhao. "GraphMemDialog: Optimizing End-to-End Task-Oriented Dialog Systems Using Graph Memory Networks". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 10 (28 de junio de 2022): 11504–12. http://dx.doi.org/10.1609/aaai.v36i10.21403.
Texto completoLiu, Yuanxin, Delei Tian y Bin Zheng. "Non-communicating Decentralized Multi-robot Collision Avoidance in Grid Graph Workspace based on Dueling Double Deep Q-Network". Journal of Physics: Conference Series 2456, n.º 1 (1 de marzo de 2023): 012015. http://dx.doi.org/10.1088/1742-6596/2456/1/012015.
Texto completoSu, Guimin, Zimu Zeng, Andi Song, Cong Zhao, Feng Shen, Liangxiao Yuan y Xinghua Li. "A General Framework for Reconstructing Full-Sample Continuous Vehicle Trajectories Using Roadside Sensing Data". Applied Sciences 13, n.º 5 (28 de febrero de 2023): 3141. http://dx.doi.org/10.3390/app13053141.
Texto completoHu, Zhangfang, Libujie Chen, Yuan Luo y Jingfan Zhou. "EEG-Based Emotion Recognition Using Convolutional Recurrent Neural Network with Multi-Head Self-Attention". Applied Sciences 12, n.º 21 (6 de noviembre de 2022): 11255. http://dx.doi.org/10.3390/app122111255.
Texto completoJi, Zhanhao, Guojiang Shen, Juntao Wang, Mario Collotta, Zhi Liu y Xiangjie Kong. "Multi-Vehicle Trajectory Tracking towards Digital Twin Intersections for Internet of Vehicles". Electronics 12, n.º 2 (5 de enero de 2023): 275. http://dx.doi.org/10.3390/electronics12020275.
Texto completoJin, Yanliang, Jinjin Ye, Liquan Shen, Yong Xiong, Lele Fan y Qingfu Zang. "Hierarchical Attention Neural Network for Event Types to Improve Event Detection". Sensors 22, n.º 11 (31 de mayo de 2022): 4202. http://dx.doi.org/10.3390/s22114202.
Texto completoTian, Qi, Kun Kuang, Furui Liu y Baoxiang Wang. "Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 10 (26 de junio de 2023): 11672–80. http://dx.doi.org/10.1609/aaai.v37i10.26379.
Texto completoYang, Shuai, Yueqin Zhang y Zehua Zhang. "Runoff Prediction Based on Dynamic Spatiotemporal Graph Neural Network". Water 15, n.º 13 (5 de julio de 2023): 2463. http://dx.doi.org/10.3390/w15132463.
Texto completoLi, Youru, Zhenfeng Zhu, Deqiang Kong, Meixiang Xu y Yao Zhao. "Learning Heterogeneous Spatial-Temporal Representation for Bike-Sharing Demand Prediction". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 2019): 1004–11. http://dx.doi.org/10.1609/aaai.v33i01.33011004.
Texto completoLiu, Lijuan, Mingxiao Wu, Rung-Ching Chen, Shunzhi Zhu y Yan Wang. "A Hybrid Deep Learning Model for Multi-Station Classification and Passenger Flow Prediction". Applied Sciences 13, n.º 5 (23 de febrero de 2023): 2899. http://dx.doi.org/10.3390/app13052899.
Texto completoChen, Yun, Chengwei Liang, Dengcheng Liu, Qingren Niu, Xinke Miao, Guangyu Dong, Liguang Li, Shanbin Liao, Xiaoci Ni y Xiaobo Huang. "Embedding-Graph-Neural-Network for Transient NOx Emissions Prediction". Energies 16, n.º 1 (20 de diciembre de 2022): 3. http://dx.doi.org/10.3390/en16010003.
Texto completoUddin, Md Azher, Joolekha Bibi Joolee, Young-Koo Lee y Kyung-Ah Sohn. "A Novel Multi-Modal Network-Based Dynamic Scene Understanding". ACM Transactions on Multimedia Computing, Communications, and Applications 18, n.º 1 (31 de enero de 2022): 1–19. http://dx.doi.org/10.1145/3462218.
Texto completoZhou, Hang, Junqing Yu y Wei Yang. "Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly Detection". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 3 (26 de junio de 2023): 3769–77. http://dx.doi.org/10.1609/aaai.v37i3.25489.
Texto completoZhang, Suqi, Xinxin Wang, Wenfeng Wang, Ningjing Zhang, Yunhao Fang y Jianxin Li. "Recommendation model based on intention decomposition and heterogeneous information fusion". Mathematical Biosciences and Engineering 20, n.º 9 (2023): 16401–20. http://dx.doi.org/10.3934/mbe.2023732.
Texto completoFang, Ziquan, Lu Pan, Lu Chen, Yuntao Du y Yunjun Gao. "MDTP". Proceedings of the VLDB Endowment 14, n.º 8 (abril de 2021): 1289–97. http://dx.doi.org/10.14778/3457390.3457394.
Texto completoXie, Yu, Yuanqiao Zhang, Maoguo Gong, Zedong Tang y Chao Han. "MGAT: Multi-view Graph Attention Networks". Neural Networks 132 (diciembre de 2020): 180–89. http://dx.doi.org/10.1016/j.neunet.2020.08.021.
Texto completoYao, Kaixuan, Jiye Liang, Jianqing Liang, Ming Li y Feilong Cao. "Multi-view graph convolutional networks with attention mechanism". Artificial Intelligence 307 (junio de 2022): 103708. http://dx.doi.org/10.1016/j.artint.2022.103708.
Texto completoChen, Lei, Jie Cao, Youquan Wang, Weichao Liang y Guixiang Zhu. "Multi-view Graph Attention Network for Travel Recommendation". Expert Systems with Applications 191 (abril de 2022): 116234. http://dx.doi.org/10.1016/j.eswa.2021.116234.
Texto completoZhang, Pengyu, Yong Zhang, Jingcheng Wang y Baocai Yin. "MVMA-GCN: Multi-view multi-layer attention graph convolutional networks". Engineering Applications of Artificial Intelligence 126 (noviembre de 2023): 106717. http://dx.doi.org/10.1016/j.engappai.2023.106717.
Texto completoPoologaindran, Anujan, Mike Hart, Tom Santarius, Stephen Price, Rohit Sinha, Mike Sughrue, Yaara Erez, Rafael Romero-Garcia y John Suckling. "Longitudinal Connectome Analyses following Low-Grade Glioma Neurosurgery: Implications for Cognitive Rehabilitation". Neuro-Oncology 23, Supplement_4 (1 de octubre de 2021): iv8. http://dx.doi.org/10.1093/neuonc/noab195.015.
Texto completoTang, Chang, Xinwang Liu, Xinzhong Zhu, En Zhu, Zhigang Luo, Lizhe Wang y Wen Gao. "CGD: Multi-View Clustering via Cross-View Graph Diffusion". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 5924–31. http://dx.doi.org/10.1609/aaai.v34i04.6052.
Texto completoZhu, Fujian y Shaojie Dai. "Multi-view Attention Mechanism Learning for POI Recommendation". Journal of Physics: Conference Series 2258, n.º 1 (1 de abril de 2022): 012041. http://dx.doi.org/10.1088/1742-6596/2258/1/012041.
Texto completoZou, Yongqi, Wenjiang Feng, Juntao Zhang y Jingfu Li. "Forecasting of Short-Term Load Using the MFF-SAM-GCN Model". Energies 15, n.º 9 (25 de abril de 2022): 3140. http://dx.doi.org/10.3390/en15093140.
Texto completoZou, Yongqi, Wenjiang Feng, Juntao Zhang y Jingfu Li. "Forecasting of Short-Term Load Using the MFF-SAM-GCN Model". Energies 15, n.º 9 (25 de abril de 2022): 3140. http://dx.doi.org/10.3390/en15093140.
Texto completoWu, Fei, Changjiang Zheng, Chen Zhang, Junze Ma y Kai Sun. "Multi-View Multi-Attention Graph Neural Network for Traffic Flow Forecasting". Applied Sciences 13, n.º 2 (4 de enero de 2023): 711. http://dx.doi.org/10.3390/app13020711.
Texto completoCui, Wanqiu, Junping Du, Dawei Wang, Feifei Kou y Zhe Xue. "MVGAN: Multi-View Graph Attention Network for Social Event Detection". ACM Transactions on Intelligent Systems and Technology 12, n.º 3 (19 de julio de 2021): 1–24. http://dx.doi.org/10.1145/3447270.
Texto completoHuang, Zongmo, Yazhou Ren, Xiaorong Pu, Shudong Huang, Zenglin Xu y Lifang He. "Self-Supervised Graph Attention Networks for Deep Weighted Multi-View Clustering". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 7 (26 de junio de 2023): 7936–43. http://dx.doi.org/10.1609/aaai.v37i7.25960.
Texto completoWang, Li, Xin Wang y Jiao Wang. "Rail Transit Prediction Based on Multi-View Graph Attention Networks". Journal of Advanced Transportation 2022 (6 de julio de 2022): 1–8. http://dx.doi.org/10.1155/2022/4672617.
Texto completoLing, Yawen, Jianpeng Chen, Yazhou Ren, Xiaorong Pu, Jie Xu, Xiaofeng Zhu y Lifang He. "Dual Label-Guided Graph Refinement for Multi-View Graph Clustering". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 7 (26 de junio de 2023): 8791–98. http://dx.doi.org/10.1609/aaai.v37i7.26057.
Texto completoLyu, Gengyu, Xiang Deng, Yanan Wu y Songhe Feng. "Beyond Shared Subspace: A View-Specific Fusion for Multi-View Multi-Label Learning". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 7 (28 de junio de 2022): 7647–54. http://dx.doi.org/10.1609/aaai.v36i7.20731.
Texto completoZhang, Pei, Siwei Wang, Jingtao Hu, Zhen Cheng, Xifeng Guo, En Zhu y Zhiping Cai. "Adaptive Weighted Graph Fusion Incomplete Multi-View Subspace Clustering". Sensors 20, n.º 20 (10 de octubre de 2020): 5755. http://dx.doi.org/10.3390/s20205755.
Texto completoShang, Chao, Qinqing Liu, Qianqian Tong, Jiangwen Sun, Minghu Song y Jinbo Bi. "Multi-view spectral graph convolution with consistent edge attention for molecular modeling". Neurocomputing 445 (julio de 2021): 12–25. http://dx.doi.org/10.1016/j.neucom.2021.02.025.
Texto completoYu, Jinshi, Qi Duan, Haonan Huang, Shude He y Tao Zou. "Effective Incomplete Multi-View Clustering via Low-Rank Graph Tensor Completion". Mathematics 11, n.º 3 (28 de enero de 2023): 652. http://dx.doi.org/10.3390/math11030652.
Texto completoHuang, Yanquan, Haoliang Yuan y Loi Lei Lai. "Latent multi-view semi-supervised classification by using graph learning". International Journal of Wavelets, Multiresolution and Information Processing 18, n.º 05 (20 de junio de 2020): 2050039. http://dx.doi.org/10.1142/s0219691320500393.
Texto completoAlothali, Eiman, Motamen Salih, Kadhim Hayawi y Hany Alashwal. "Bot-MGAT: A Transfer Learning Model Based on a Multi-View Graph Attention Network to Detect Social Bots". Applied Sciences 12, n.º 16 (13 de agosto de 2022): 8117. http://dx.doi.org/10.3390/app12168117.
Texto completoZeng, Hui, Tianmeng Zhao, Ruting Cheng, Fuzhou Wang y Jiwei Liu. "Hierarchical Graph Attention Based Multi-View Convolutional Neural Network for 3D Object Recognition". IEEE Access 9 (2021): 33323–35. http://dx.doi.org/10.1109/access.2021.3059853.
Texto completoZhu, Jiangqiang, Kai Li, Jinjia Peng y Jing Qi. "Self-Supervised Graph Attention Collaborative Filtering for Recommendation". Electronics 12, n.º 4 (5 de febrero de 2023): 793. http://dx.doi.org/10.3390/electronics12040793.
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