Zeitschriftenartikel zum Thema „Variational graph auto-Encoder (VGAE)“
Geben Sie eine Quelle nach APA, MLA, Chicago, Harvard und anderen Zitierweisen an
Machen Sie sich mit Top-47 Zeitschriftenartikel für die Forschung zum Thema "Variational graph auto-Encoder (VGAE)" bekannt.
Neben jedem Werk im Literaturverzeichnis ist die Option "Zur Bibliographie hinzufügen" verfügbar. Nutzen Sie sie, wird Ihre bibliographische Angabe des gewählten Werkes nach der nötigen Zitierweise (APA, MLA, Harvard, Chicago, Vancouver usw.) automatisch gestaltet.
Sie können auch den vollen Text der wissenschaftlichen Publikation im PDF-Format herunterladen und eine Online-Annotation der Arbeit lesen, wenn die relevanten Parameter in den Metadaten verfügbar sind.
Sehen Sie die Zeitschriftenartikel für verschiedene Spezialgebieten durch und erstellen Sie Ihre Bibliographie auf korrekte Weise.
Hui, Binyuan, Pengfei Zhu und Qinghua Hu. „Collaborative Graph Convolutional Networks: Unsupervised Learning Meets Semi-Supervised Learning“. Proceedings of the AAAI Conference on Artificial Intelligence 34, Nr. 04 (03.04.2020): 4215–22. http://dx.doi.org/10.1609/aaai.v34i04.5843.
Der volle Inhalt der QuelleDuan, Yuning, Jingdong Jia, Yuhui Jin, Haitian Zhang und Jian Huang. „Expressway Vehicle Trajectory Prediction Based on Fusion Data of Trajectories and Maps from Vehicle Perspective“. Applied Sciences 14, Nr. 10 (15.05.2024): 4181. http://dx.doi.org/10.3390/app14104181.
Der volle Inhalt der QuelleChoong, Jun Jin, Xin Liu und Tsuyoshi Murata. „Optimizing Variational Graph Autoencoder for Community Detection with Dual Optimization“. Entropy 22, Nr. 2 (07.02.2020): 197. http://dx.doi.org/10.3390/e22020197.
Der volle Inhalt der QuelleMa, Weigang, Jing Wang, Chaohui Zhang, Qiao Jia, Lei Zhu, Wenjiang Ji und Zhoukai Wang. „Application of Variational Graph Autoencoder in Traction Control of Energy-Saving Driving for High-Speed Train“. Applied Sciences 14, Nr. 5 (29.02.2024): 2037. http://dx.doi.org/10.3390/app14052037.
Der volle Inhalt der QuelleZhang, Jing, Guangli Wu und Shanshan Song. „Video Summarization Generation Based on Graph Structure Reconstruction“. Electronics 12, Nr. 23 (23.11.2023): 4757. http://dx.doi.org/10.3390/electronics12234757.
Der volle Inhalt der QuelleZhang, Ying, Qi Zhang, Yu Zhang und Zhiyuan Zhu. „VGAE-AMF: A Novel Topology Reconstruction Algorithm for Invulnerability of Ocean Wireless Sensor Networks Based on Graph Neural Network“. Journal of Marine Science and Engineering 11, Nr. 4 (16.04.2023): 843. http://dx.doi.org/10.3390/jmse11040843.
Der volle Inhalt der QuellePatel, Neel, Nhat Le, Tan Nguyen, Fedaa Najdawi, Sandhya Srinivasan, Adam Stanford-Moore, Deeksha Kartik et al. „Abstract 4912: Unsupervised detection of stromal phenotypes with distinct fibrogenic and inflamed properties in NSCLC“. Cancer Research 84, Nr. 6_Supplement (22.03.2024): 4912. http://dx.doi.org/10.1158/1538-7445.am2024-4912.
Der volle Inhalt der QuelleShi, Han, Haozheng Fan und James T. Kwok. „Effective Decoding in Graph Auto-Encoder Using Triadic Closure“. Proceedings of the AAAI Conference on Artificial Intelligence 34, Nr. 01 (03.04.2020): 906–13. http://dx.doi.org/10.1609/aaai.v34i01.5437.
Der volle Inhalt der QuelleBehrouzi, Tina, und Dimitrios Hatzinakos. „Graph variational auto-encoder for deriving EEG-based graph embedding“. Pattern Recognition 121 (Januar 2022): 108202. http://dx.doi.org/10.1016/j.patcog.2021.108202.
Der volle Inhalt der QuelleZhan, Junjian, Feng Li, Yang Wang, Daoyu Lin und Guangluan Xu. „Structural Adversarial Variational Auto-Encoder for Attributed Network Embedding“. Applied Sciences 11, Nr. 5 (07.03.2021): 2371. http://dx.doi.org/10.3390/app11052371.
Der volle Inhalt der QuelleXie, Luodi, Huimin Huang und Qing Du. „A Co-Embedding Model with Variational Auto-Encoder for Knowledge Graphs“. Applied Sciences 12, Nr. 2 (12.01.2022): 715. http://dx.doi.org/10.3390/app12020715.
Der volle Inhalt der Quellefathy,, Asmaa Mohamed. „Deep Embedding Data Fusion Scheme Using Variational Graph Auto-Encoder in IoT Environments“. International Journal of Advanced Trends in Computer Science and Engineering 9, Nr. 4 (25.08.2020): 4363–72. http://dx.doi.org/10.30534/ijatcse/2020/28942020.
Der volle Inhalt der QuelleZhao, Yuexuan, und Jing Huang. „Dirichlet Process Prior for Student’s t Graph Variational Autoencoders“. Future Internet 13, Nr. 3 (16.03.2021): 75. http://dx.doi.org/10.3390/fi13030075.
Der volle Inhalt der QuelleYao, Heng, Jihong Guan und Tianying Liu. „Denoising Protein–Protein interaction network via variational graph auto-encoder for protein complex detection“. Journal of Bioinformatics and Computational Biology 18, Nr. 03 (Juni 2020): 2040010. http://dx.doi.org/10.1142/s0219720020400107.
Der volle Inhalt der QuelleZhou, Qiang, Xinjiang Lu, Jingjing Gu, Zhe Zheng, Bo Jin und Jingbo Zhou. „Explainable Origin-Destination Crowd Flow Interpolation via Variational Multi-Modal Recurrent Graph Auto-Encoder“. Proceedings of the AAAI Conference on Artificial Intelligence 38, Nr. 8 (24.03.2024): 9422–30. http://dx.doi.org/10.1609/aaai.v38i8.28796.
Der volle Inhalt der QuelleKarimi, Mostafa, Arman Hasanzadeh und Yang Shen. „Network-principled deep generative models for designing drug combinations as graph sets“. Bioinformatics 36, Supplement_1 (01.07.2020): i445—i454. http://dx.doi.org/10.1093/bioinformatics/btaa317.
Der volle Inhalt der QuelleSu, Hang, Xinzheng Zhang, Yuqing Luo, Ce Zhang, Xichuan Zhou und Peter M. Atkinson. „Nonlocal feature learning based on a variational graph auto-encoder network for small area change detection using SAR imagery“. ISPRS Journal of Photogrammetry and Remote Sensing 193 (November 2022): 137–49. http://dx.doi.org/10.1016/j.isprsjprs.2022.09.006.
Der volle Inhalt der QuelleXu, Lei, Leiming Xia, Shourun Pan und Zhen Li. „Triple Generative Self-Supervised Learning Method for Molecular Property Prediction“. International Journal of Molecular Sciences 25, Nr. 7 (28.03.2024): 3794. http://dx.doi.org/10.3390/ijms25073794.
Der volle Inhalt der QuelleDu, Bing, Xiaomu Cheng, Yiping Duan und Huansheng Ning. „fMRI Brain Decoding and Its Applications in Brain–Computer Interface: A Survey“. Brain Sciences 12, Nr. 2 (07.02.2022): 228. http://dx.doi.org/10.3390/brainsci12020228.
Der volle Inhalt der QuelleWang, Lei, Zejian Yuan und Badong Chen. „Learning to Generate an Unbiased Scene Graph by Using Attribute-Guided Predicate Features“. Proceedings of the AAAI Conference on Artificial Intelligence 37, Nr. 2 (26.06.2023): 2581–89. http://dx.doi.org/10.1609/aaai.v37i2.25356.
Der volle Inhalt der QuelleMao, Cunli, Haoyuan Liang, Zhengtao Yu, Yuxin Huang und Junjun Guo. „A Clustering Method of Case-Involved News by Combining Topic Network and Multi-Head Attention Mechanism“. Sensors 21, Nr. 22 (11.11.2021): 7501. http://dx.doi.org/10.3390/s21227501.
Der volle Inhalt der QuelleZhao, Mingle, Dingfu Zhou, Xibin Song, Xiuwan Chen und Liangjun Zhang. „DiT-SLAM: Real-Time Dense Visual-Inertial SLAM with Implicit Depth Representation and Tightly-Coupled Graph Optimization“. Sensors 22, Nr. 9 (28.04.2022): 3389. http://dx.doi.org/10.3390/s22093389.
Der volle Inhalt der QuelleLi, Peng, Shufang Guo, Chenghao Zhang, Mosharaf Md Parvej und Jing Zhang. „A Construction Method for a Dynamic Weighted Protein Network Using Multi-Level Embedding“. Applied Sciences 14, Nr. 10 (11.05.2024): 4090. http://dx.doi.org/10.3390/app14104090.
Der volle Inhalt der QuelleZhu, Guixiang, Jie Cao, Lei Chen, Youquan Wang, Zhan Bu, Shuxin Yang, Jianqing Wu und Zhiping Wang. „A Multi-task Graph Neural Network with Variational Graph Auto-Encoders for Session-based Travel Packages Recommendation“. ACM Transactions on the Web, Februar 2023. http://dx.doi.org/10.1145/3577032.
Der volle Inhalt der QuelleLi, Dongjie, Dong Li und Guang Lian. „Variational Graph Autoencoder with Adversarial Mutual Information Learning for Network Representation Learning“. ACM Transactions on Knowledge Discovery from Data, 22.08.2022. http://dx.doi.org/10.1145/3555809.
Der volle Inhalt der QuelleYuan, Wei, Shiyu Zhao, Li Wang, Lijia Cai und Yong Zhang. „Online course evaluation model based on graph auto-encoder“. Intelligent Data Analysis, 21.03.2024, 1–23. http://dx.doi.org/10.3233/ida-230557.
Der volle Inhalt der QuelleLi, Dongjie, Dong Li und Guang Lian. „Variational Graph Autoencoder with Mutual Information Maximization for Graph Representations Learning“. International Journal of Pattern Recognition and Artificial Intelligence, 08.06.2022. http://dx.doi.org/10.1142/s0218001422520127.
Der volle Inhalt der QuelleIwata, Hiroaki, Taichi Nakai, Takuto Koyama, Shigeyuki Matsumoto, Ryosuke Kojima und Yasushi Okuno. „VGAE-MCTS: A New Molecular Generative Model Combining the Variational Graph Auto-Encoder and Monte Carlo Tree Search“. Journal of Chemical Information and Modeling, 22.11.2023. http://dx.doi.org/10.1021/acs.jcim.3c01220.
Der volle Inhalt der QuelleLiu, Zhi, Yang Chen, Feng Xia, Jixin Bian, Bing Zhu, Guojiang Shen und Xiangjie Kong. „TAP: Traffic Accident Profiling via Multi-task Spatio-Temporal Graph Representation Learning“. ACM Transactions on Knowledge Discovery from Data, 22.09.2022. http://dx.doi.org/10.1145/3564594.
Der volle Inhalt der QuelleLi, Bo, Chen Peng, Zeran You, Xiaolong Zhang und Shihua Zhang. „Single-cell RNA-sequencing data clustering using variational graph attention auto-encoder with self-supervised leaning“. Briefings in Bioinformatics 24, Nr. 6 (22.09.2023). http://dx.doi.org/10.1093/bib/bbad383.
Der volle Inhalt der QuelleDuy Nguyen, Viet Thanh, und Truong Son Hy. „Multimodal pretraining for unsupervised protein representation learning“. Biology Methods and Protocols, 18.06.2024. http://dx.doi.org/10.1093/biomethods/bpae043.
Der volle Inhalt der QuelleYi, Jing, und Zhenzhong Chen. „Multi-modal Variational Graph Auto-encoder for Recommendation Systems“. IEEE Transactions on Multimedia, 2021, 1. http://dx.doi.org/10.1109/tmm.2021.3111487.
Der volle Inhalt der QuelleMrabah, Nairouz, Mohamed Bouguessa und Riadh Ksantini. „A contrastive variational graph auto-encoder for node clustering“. Pattern Recognition, Dezember 2023, 110209. http://dx.doi.org/10.1016/j.patcog.2023.110209.
Der volle Inhalt der QuelleZhang, Yi, Yiwen Zhang, Dengcheng Yan, Shuiguang Deng und Yun Yang. „Revisiting Graph-based Recommender Systems from the Perspective of Variational Auto-Encoder“. ACM Transactions on Information Systems, Dezember 2022. http://dx.doi.org/10.1145/3573385.
Der volle Inhalt der QuelleZhou, Xin, und Chunyan Miao. „Disentangled Graph Variational Auto-Encoder for Multimodal Recommendation With Interpretability“. IEEE Transactions on Multimedia, 2024, 1–13. http://dx.doi.org/10.1109/tmm.2024.3369875.
Der volle Inhalt der QuelleYi, Jing, Xubin Ren und Zhenzhong Chen. „Multi-Auxiliary Augmented Collaborative Variational Auto-encoder for Tag Recommendation“. ACM Transactions on Information Systems, 31.01.2023. http://dx.doi.org/10.1145/3578932.
Der volle Inhalt der QuelleChen, Han, Hanchen Wang, Hongmei Chen, Ying Zhang, Wenjie Zhang und Xuemin Lin. „Denoising Variational Graph of Graphs Auto-Encoder for Predicting Structured Entity Interactions“. IEEE Transactions on Knowledge and Data Engineering, 2023, 1–14. http://dx.doi.org/10.1109/tkde.2023.3298490.
Der volle Inhalt der QuelleZhu, Yuan, Feng Zhang, Shihua Zhang und Ming Yi. „Predicting latent lncRNA and cancer metastatic event associations via variational graph auto-encoder“. Methods, Januar 2023. http://dx.doi.org/10.1016/j.ymeth.2023.01.006.
Der volle Inhalt der QuelleGervits, Asia, und Roded Sharan. „Predicting genetic interactions, cell line dependencies and drug sensitivities with variational graph auto-encoder“. Frontiers in Bioinformatics 2 (02.12.2022). http://dx.doi.org/10.3389/fbinf.2022.1025783.
Der volle Inhalt der QuelleDing, Yulian, Xiujuan Lei, Bo Liao und Fangxiang Wu. „Predicting miRNA-Disease Associations Based on Multi-View Variational Graph Auto-Encoder with Matrix Factorization“. IEEE Journal of Biomedical and Health Informatics, 2021, 1. http://dx.doi.org/10.1109/jbhi.2021.3088342.
Der volle Inhalt der QuelleFu, Yao, Runtao Yang und Lina Zhang. „Association prediction of CircRNAs and diseases using multi-homogeneous graphs and variational graph auto-encoder“. Computers in Biology and Medicine, November 2022, 106289. http://dx.doi.org/10.1016/j.compbiomed.2022.106289.
Der volle Inhalt der QuelleAftab, Rukhma, Yan Qiang, Juanjuan Zhao, Zia Urrehman und Zijuan Zhao. „Graph Neural Network for representation learning of lung cancer“. BMC Cancer 23, Nr. 1 (26.10.2023). http://dx.doi.org/10.1186/s12885-023-11516-8.
Der volle Inhalt der QuelleNgo, Nhat Khang, und Truong Son Hy. „Multimodal Protein Representation Learning and Target-aware Variational Auto-encoders for Protein-binding Ligand Generation“. Machine Learning: Science and Technology, 15.04.2024. http://dx.doi.org/10.1088/2632-2153/ad3ee4.
Der volle Inhalt der QuelleZhang, Yihao, Yuhao Wang, Wei Zhou, Pengxiang Lan, Haoran Xiang, Junlin Zhu und Meng Yuan. „Conversational recommender based on graph sparsification and multi-hop attention“. Intelligent Data Analysis, 14.09.2023, 1–21. http://dx.doi.org/10.3233/ida-230148.
Der volle Inhalt der QuelleLi, Yunyi, Yongjing Hao, Pengpeng Zhao, Guanfeng Liu, Yanchi Liu, Victor S. Sheng und Xiaofang Zhou. „Edge-Enhanced Global Disentangled Graph Neural Network for Sequential Recommendation“. ACM Transactions on Knowledge Discovery from Data, 06.02.2023. http://dx.doi.org/10.1145/3577928.
Der volle Inhalt der QuellePeng, Lihong, Liangliang Huang, Qiongli Su, Geng Tian, Min Chen und Guosheng Han. „LDA-VGHB: identifying potential lncRNA–disease associations with singular value decomposition, variational graph auto-encoder and heterogeneous Newton boosting machine“. Briefings in Bioinformatics 25, Nr. 1 (22.11.2023). http://dx.doi.org/10.1093/bib/bbad466.
Der volle Inhalt der QuelleBhavna, Km, Azman Akhter, Romi Banerjee und Dipanjan Roy. „Explainable deep-learning framework: decoding brain states and prediction of individual performance in false-belief task at early childhood stage“. Frontiers in Neuroinformatics 18 (28.06.2024). http://dx.doi.org/10.3389/fninf.2024.1392661.
Der volle Inhalt der Quelle