Artigos de revistas sobre o tema "Graph attention network (GAT)"
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Wu, Nan, e Chaofan Wang. "Ensemble Graph Attention Networks". Transactions on Machine Learning and Artificial Intelligence 10, n.º 3 (12 de junho de 2022): 29–41. http://dx.doi.org/10.14738/tmlai.103.12399.
Texto completo da fonteVerma, Atul Kumar, Rahul Saxena, Mahipal Jadeja, Vikrant Bhateja e Jerry Chun-Wei Lin. "Bet-GAT: An Efficient Centrality-Based Graph Attention Model for Semi-Supervised Node Classification". Applied Sciences 13, n.º 2 (7 de janeiro de 2023): 847. http://dx.doi.org/10.3390/app13020847.
Texto completo da fonteLu, Shengfu, Jiaming Kang, Jinyu Zhang e Mi Li. "Assessment method of depressive disorder level based on graph attention network". ITM Web of Conferences 45 (2022): 01039. http://dx.doi.org/10.1051/itmconf/20224501039.
Texto completo da fonteXiang, Zhijie, Weijia Gong, Zehui Li, Xue Yang, Jihua Wang e Hong Wang. "Predicting Protein–Protein Interactions via Gated Graph Attention Signed Network". Biomolecules 11, n.º 6 (28 de maio de 2021): 799. http://dx.doi.org/10.3390/biom11060799.
Texto completo da fonteYuan, Hong, Jing Huang e Jin Li. "Protein-ligand binding affinity prediction model based on graph attention network". Mathematical Biosciences and Engineering 18, n.º 6 (2021): 9148–62. http://dx.doi.org/10.3934/mbe.2021451.
Texto completo da fonteJing, Weipeng, Xianyang Song, Donglin Di e Houbing Song. "geoGAT: Graph Model Based on Attention Mechanism for Geographic Text Classification". ACM Transactions on Asian and Low-Resource Language Information Processing 20, n.º 5 (30 de setembro de 2021): 1–18. http://dx.doi.org/10.1145/3434239.
Texto completo da fonteLiu, Yiwen, Tao Wen e Zhenning Wu. "Motion Artifact Detection Based on Regional–Temporal Graph Attention Network from Head Computed Tomography Images". Electronics 13, n.º 4 (10 de fevereiro de 2024): 724. http://dx.doi.org/10.3390/electronics13040724.
Texto completo da fonteHuang, Ling, Xing-Xing Liu, Shu-Qiang Huang, Chang-Dong Wang, Wei Tu, Jia-Meng Xie, Shuai Tang e Wendi Xie. "Temporal Hierarchical Graph Attention Network for Traffic Prediction". ACM Transactions on Intelligent Systems and Technology 12, n.º 6 (31 de dezembro de 2021): 1–21. http://dx.doi.org/10.1145/3446430.
Texto completo da fonteSong, Kyungwoo, Yohan Jung, Dongjun Kim e Il-Chul Moon. "Implicit Kernel Attention". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 11 (18 de maio de 2021): 9713–21. http://dx.doi.org/10.1609/aaai.v35i11.17168.
Texto completo da fonteZheng, Jing, Ziren Gao, Jingsong Ma, Jie Shen e Kang Zhang. "Deep Graph Convolutional Networks for Accurate Automatic Road Network Selection". ISPRS International Journal of Geo-Information 10, n.º 11 (11 de novembro de 2021): 768. http://dx.doi.org/10.3390/ijgi10110768.
Texto completo da fonteLiu, Tong, e Bojun Liu. "Next basket recommendation based on graph attention network and transformer". Journal of Physics: Conference Series 2303, n.º 1 (1 de julho de 2022): 012023. http://dx.doi.org/10.1088/1742-6596/2303/1/012023.
Texto completo da fonteZhu, Taomei, Maria Jesus Lopez Boada e Beatriz Lopez Boada. "Adaptive Graph Attention and Long Short-Term Memory-Based Networks for Traffic Prediction". Mathematics 12, n.º 2 (12 de janeiro de 2024): 255. http://dx.doi.org/10.3390/math12020255.
Texto completo da fonteDeng, Xuan, Cheng Zhang, Jian Shi e Zizhao Wu. "PU-GAT: Point cloud upsampling with graph attention network". Graphical Models 130 (dezembro de 2023): 101201. http://dx.doi.org/10.1016/j.gmod.2023.101201.
Texto completo da fonteZhao, Yanna, Gaobo Zhang, Changxu Dong, Qi Yuan, Fangzhou Xu e Yuanjie Zheng. "Graph Attention Network with Focal Loss for Seizure Detection on Electroencephalography Signals". International Journal of Neural Systems 31, n.º 07 (18 de maio de 2021): 2150027. http://dx.doi.org/10.1142/s0129065721500271.
Texto completo da fontePu, S., Y. Song, Y. Chen, Y. Li, J. Zhang, Q. Lin, X. Zhu et al. "HYPERSPECTRAL IMAGE CLASSIFICATION WITH LOCALIZED SPECTRAL FILTERING-BASED GRAPH ATTENTION NETWORK". ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2022 (17 de maio de 2022): 155–61. http://dx.doi.org/10.5194/isprs-annals-v-3-2022-155-2022.
Texto completo da fonteWang, Renping, Shun Li, Enhao Tang, Sen Lan, Yajing Liu, Jing Yang, Shizhen Huang e Hailong Hu. "SH-GAT: Software-hardware co-design for accelerating graph attention networks on FPGA". Electronic Research Archive 32, n.º 4 (2024): 2310–22. http://dx.doi.org/10.3934/era.2024105.
Texto completo da fonteLin, Yu-Chen, Chia-Hung Wang e Yu-Cheng Lin. "GAT TransPruning: progressive channel pruning strategy combining graph attention network and transformer". PeerJ Computer Science 10 (23 de abril de 2024): e2012. http://dx.doi.org/10.7717/peerj-cs.2012.
Texto completo da fonteBian, Chen, Xiu-Juan Lei e Fang-Xiang Wu. "GATCDA: Predicting circRNA-Disease Associations Based on Graph Attention Network". Cancers 13, n.º 11 (25 de maio de 2021): 2595. http://dx.doi.org/10.3390/cancers13112595.
Texto completo da fonteZhao, Mengmeng, Haipeng Peng, Lixiang Li e Yeqing Ren. "Graph Attention Network and Informer for Multivariate Time Series Anomaly Detection". Sensors 24, n.º 5 (26 de fevereiro de 2024): 1522. http://dx.doi.org/10.3390/s24051522.
Texto completo da fonteTanvir, Raihanul Bari, Md Mezbahul Islam, Masrur Sobhan, Dongsheng Luo e Ananda Mohan Mondal. "MOGAT: A Multi-Omics Integration Framework Using Graph Attention Networks for Cancer Subtype Prediction". International Journal of Molecular Sciences 25, n.º 5 (28 de fevereiro de 2024): 2788. http://dx.doi.org/10.3390/ijms25052788.
Texto completo da fonteLv, Shaoqing, Jungang Dong, Chichi Wang, Xuanhong Wang e Zhiqiang Bao. "RB-GAT: A Text Classification Model Based on RoBERTa-BiGRU with Graph ATtention Network". Sensors 24, n.º 11 (24 de maio de 2024): 3365. http://dx.doi.org/10.3390/s24113365.
Texto completo da fonteLei, Zengyu, Caiming Zhang, Yunyang Xu e Xuemei Li. "DR-GAT: Dynamic routing graph attention network for stock recommendation". Information Sciences 654 (janeiro de 2024): 119833. http://dx.doi.org/10.1016/j.ins.2023.119833.
Texto completo da fonteWan, Qizhi, Changxuan Wan, Keli Xiao, Kun Lu, Chenliang Li, Xiping Liu e Dexi Liu. "Dependency Structure-Enhanced Graph Attention Networks for Event Detection". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 17 (24 de março de 2024): 19098–106. http://dx.doi.org/10.1609/aaai.v38i17.29877.
Texto completo da fonteYang, Wu-Lue, Xiao-Ze Chen e Xu-Hua Yang. "Semisupervised Classification with High-Order Graph Learning Attention Neural Network". Mathematical Problems in Engineering 2021 (7 de dezembro de 2021): 1–10. http://dx.doi.org/10.1155/2021/3911137.
Texto completo da fonteCai, Fengze, Qiang Hu, Renjie Zhou e Neal Xiong. "REEGAT: RoBERTa Entity Embedding and Graph Attention Networks Enhanced Sentence Representation for Relation Extraction". Electronics 12, n.º 11 (27 de maio de 2023): 2429. http://dx.doi.org/10.3390/electronics12112429.
Texto completo da fonteCui, Wei, Xin He, Meng Yao, Ziwei Wang, Yuanjie Hao, Jie Li, Weijie Wu et al. "Knowledge and Spatial Pyramid Distance-Based Gated Graph Attention Network for Remote Sensing Semantic Segmentation". Remote Sensing 13, n.º 7 (30 de março de 2021): 1312. http://dx.doi.org/10.3390/rs13071312.
Texto completo da fonteCao, Hailin, Wang Zhu, Wenjuan Feng e Jin Fan. "Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications". Entropy 24, n.º 3 (24 de fevereiro de 2022): 326. http://dx.doi.org/10.3390/e24030326.
Texto completo da fonteYang, Xiaohui, Hailong Ma e Miao Wang. "Research on Rumor Detection Based on a Graph Attention Network With Temporal Features". International Journal of Data Warehousing and Mining 19, n.º 2 (2 de março de 2023): 1–17. http://dx.doi.org/10.4018/ijdwm.319342.
Texto completo da fonteCao, Ruifen, Chuan He, Pijing Wei, Yansen Su, Junfeng Xia e Chunhou Zheng. "Prediction of circRNA-Disease Associations Based on the Combination of Multi-Head Graph Attention Network and Graph Convolutional Network". Biomolecules 12, n.º 7 (2 de julho de 2022): 932. http://dx.doi.org/10.3390/biom12070932.
Texto completo da fonteYağci, Mehmet Yavuz, e Muhammed Ali Aydin. "EA-GAT: Event aware graph attention network on cyber-physical systems". Computers in Industry 159-160 (agosto de 2024): 104097. http://dx.doi.org/10.1016/j.compind.2024.104097.
Texto completo da fonteZhang, Yuhang, Yaoqun Xu e Yu Zhang. "A Graph Neural Network Node Classification Application Model with Enhanced Node Association". Applied Sciences 13, n.º 12 (15 de junho de 2023): 7150. http://dx.doi.org/10.3390/app13127150.
Texto completo da fonteYe, Haonan, e Xiao Luo. "Cascading Failure Analysis on Shanghai Metro Networks: An Improved Coupled Map Lattices Model Based on Graph Attention Networks". International Journal of Environmental Research and Public Health 19, n.º 1 (25 de dezembro de 2021): 204. http://dx.doi.org/10.3390/ijerph19010204.
Texto completo da fonteLi, Yansheng, Ruixian Chen, Yongjun Zhang, Mi Zhang e Ling Chen. "Multi-Label Remote Sensing Image Scene Classification by Combining a Convolutional Neural Network and a Graph Neural Network". Remote Sensing 12, n.º 23 (7 de dezembro de 2020): 4003. http://dx.doi.org/10.3390/rs12234003.
Texto completo da fonteMu, Jichong, Jihong Ouyang, Yachen Yao e Zongxiao Ren. "Span-Prototype Graph Based on Graph Attention Network for Nested Named Entity Recognition". Electronics 12, n.º 23 (23 de novembro de 2023): 4753. http://dx.doi.org/10.3390/electronics12234753.
Texto completo da fonteYang, Xiaohui, Hailong Ma e Miao Wang. "Rumor Detection with Bidirectional Graph Attention Networks". Security and Communication Networks 2022 (18 de janeiro de 2022): 1–13. http://dx.doi.org/10.1155/2022/4840997.
Texto completo da fonteJi, Cunmei, Zhihao Liu, Yutian Wang, Jiancheng Ni e Chunhou Zheng. "GATNNCDA: A Method Based on Graph Attention Network and Multi-Layer Neural Network for Predicting circRNA-Disease Associations". International Journal of Molecular Sciences 22, n.º 16 (7 de agosto de 2021): 8505. http://dx.doi.org/10.3390/ijms22168505.
Texto completo da fonteBaul, Sudipto, Khandakar Tanvir Ahmed, Joseph Filipek e Wei Zhang. "omicsGAT: Graph Attention Network for Cancer Subtype Analyses". International Journal of Molecular Sciences 23, n.º 18 (6 de setembro de 2022): 10220. http://dx.doi.org/10.3390/ijms231810220.
Texto completo da fonteWu, Xingping, Qiheng Yuan, Chunlei Zhou, Xiang Chen, Donghai Xuan e Jinwei Song. "Carbon emissions forecasting based on temporal graph transformer-based attentional neural network". Journal of Computational Methods in Sciences and Engineering 24, n.º 3 (17 de junho de 2024): 1405–21. http://dx.doi.org/10.3233/jcm-247139.
Texto completo da fonteAlothali, Eiman, Motamen Salih, Kadhim Hayawi e 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 completo da fonteWei, Pengfei, Bi Zeng e Wenxiong Liao. "Joint intent detection and slot filling with wheel-graph attention networks". Journal of Intelligent & Fuzzy Systems 42, n.º 3 (2 de fevereiro de 2022): 2409–20. http://dx.doi.org/10.3233/jifs-211674.
Texto completo da fonteZhao, Mingxiu, Jing Zhang, Qin Li, Junzheng Yang, Estevao Siga e Tianchi Zhang. "GAT-ABiGRU Based Prediction Model for AUV Trajectory". Applied Sciences 14, n.º 10 (15 de maio de 2024): 4184. http://dx.doi.org/10.3390/app14104184.
Texto completo da fonteUmair, Muhammad, Iftikhar Alam, Atif Khan, Inayat Khan, Niamat Ullah e Mohammad Yusuf Momand. "N-GPETS: Neural Attention Graph-Based Pretrained Statistical Model for Extractive Text Summarization". Computational Intelligence and Neuroscience 2022 (22 de novembro de 2022): 1–14. http://dx.doi.org/10.1155/2022/6241373.
Texto completo da fonteChen, Yang, Weibing Wan, Jimi Hu, Yuxuan Wang e Bo Huang. "Complex Causal Extraction of Fusion of Entity Location Sensing and Graph Attention Networks". Information 13, n.º 8 (31 de julho de 2022): 364. http://dx.doi.org/10.3390/info13080364.
Texto completo da fonteZhou, Hang, Weikun Wang, Jiayun Jin, Zengwei Zheng e Binbin Zhou. "Graph Neural Network for Protein–Protein Interaction Prediction: A Comparative Study". Molecules 27, n.º 18 (19 de setembro de 2022): 6135. http://dx.doi.org/10.3390/molecules27186135.
Texto completo da fonteShao, Yingzhao, Yunsong Li, Li Li, Yuanle Wang, Yuchen Yang, Yueli Ding, Mingming Zhang, Yang Liu e Xiangqiang Gao. "RANet: Relationship Attention for Hyperspectral Anomaly Detection". Remote Sensing 15, n.º 23 (30 de novembro de 2023): 5570. http://dx.doi.org/10.3390/rs15235570.
Texto completo da fonteGao, Yunmeng, Liang Zhao, Jin Du e Junnan Wang. "Spatial-temporal Traffic Flow Prediction Model Based on the GAT and BiGRU". Journal of Physics: Conference Series 2589, n.º 1 (1 de setembro de 2023): 012024. http://dx.doi.org/10.1088/1742-6596/2589/1/012024.
Texto completo da fonteXu, Dawei, Qing Liu, Liehuang Zhu, Zhonghua Tan, Feng Gao e Jian Zhao. "GCNRDM: A Social Network Rumor Detection Method Based on Graph Convolutional Network in Mobile Computing". Wireless Communications and Mobile Computing 2021 (8 de outubro de 2021): 1–11. http://dx.doi.org/10.1155/2021/1690669.
Texto completo da fontePeng, Feifei, Wei Lu, Wenxia Tan, Kunlun Qi, Xiaokang Zhang e Quansheng Zhu. "Multi-Output Network Combining GNN and CNN for Remote Sensing Scene Classification". Remote Sensing 14, n.º 6 (18 de março de 2022): 1478. http://dx.doi.org/10.3390/rs14061478.
Texto completo da fonteCui, Wei, Yuanjie Hao, Xing Xu, Zhanyun Feng, Huilin Zhao, Cong Xia e Jin Wang. "Remote Sensing Scene Graph and Knowledge Graph Matching with Parallel Walking Algorithm". Remote Sensing 14, n.º 19 (29 de setembro de 2022): 4872. http://dx.doi.org/10.3390/rs14194872.
Texto completo da fonteZhan, Huixin, Kun Zhang, Keyi Lu e Victor S. Sheng. "Measuring the Privacy Leakage via Graph Reconstruction Attacks on Simplicial Neural Networks (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 13 (26 de junho de 2023): 16380–81. http://dx.doi.org/10.1609/aaai.v37i13.27050.
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