Статті в журналах з теми "Variational graph auto-Encoder (VGAE)"
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Hui, Binyuan, Pengfei Zhu, and Qinghua Hu. "Collaborative Graph Convolutional Networks: Unsupervised Learning Meets Semi-Supervised Learning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4215–22. http://dx.doi.org/10.1609/aaai.v34i04.5843.
Duan, Yuning, Jingdong Jia, Yuhui Jin, Haitian Zhang, and Jian Huang. "Expressway Vehicle Trajectory Prediction Based on Fusion Data of Trajectories and Maps from Vehicle Perspective." Applied Sciences 14, no. 10 (May 15, 2024): 4181. http://dx.doi.org/10.3390/app14104181.
Choong, Jun Jin, Xin Liu, and Tsuyoshi Murata. "Optimizing Variational Graph Autoencoder for Community Detection with Dual Optimization." Entropy 22, no. 2 (February 7, 2020): 197. http://dx.doi.org/10.3390/e22020197.
Ma, Weigang, Jing Wang, Chaohui Zhang, Qiao Jia, Lei Zhu, Wenjiang Ji, and Zhoukai Wang. "Application of Variational Graph Autoencoder in Traction Control of Energy-Saving Driving for High-Speed Train." Applied Sciences 14, no. 5 (February 29, 2024): 2037. http://dx.doi.org/10.3390/app14052037.
Zhang, Jing, Guangli Wu, and Shanshan Song. "Video Summarization Generation Based on Graph Structure Reconstruction." Electronics 12, no. 23 (November 23, 2023): 4757. http://dx.doi.org/10.3390/electronics12234757.
Zhang, Ying, Qi Zhang, Yu Zhang, and 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, no. 4 (April 16, 2023): 843. http://dx.doi.org/10.3390/jmse11040843.
Patel, 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, no. 6_Supplement (March 22, 2024): 4912. http://dx.doi.org/10.1158/1538-7445.am2024-4912.
Shi, Han, Haozheng Fan, and James T. Kwok. "Effective Decoding in Graph Auto-Encoder Using Triadic Closure." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 906–13. http://dx.doi.org/10.1609/aaai.v34i01.5437.
Behrouzi, Tina, and Dimitrios Hatzinakos. "Graph variational auto-encoder for deriving EEG-based graph embedding." Pattern Recognition 121 (January 2022): 108202. http://dx.doi.org/10.1016/j.patcog.2021.108202.
Zhan, Junjian, Feng Li, Yang Wang, Daoyu Lin, and Guangluan Xu. "Structural Adversarial Variational Auto-Encoder for Attributed Network Embedding." Applied Sciences 11, no. 5 (March 7, 2021): 2371. http://dx.doi.org/10.3390/app11052371.
Xie, Luodi, Huimin Huang, and Qing Du. "A Co-Embedding Model with Variational Auto-Encoder for Knowledge Graphs." Applied Sciences 12, no. 2 (January 12, 2022): 715. http://dx.doi.org/10.3390/app12020715.
fathy,, 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, no. 4 (August 25, 2020): 4363–72. http://dx.doi.org/10.30534/ijatcse/2020/28942020.
Zhao, Yuexuan, and Jing Huang. "Dirichlet Process Prior for Student’s t Graph Variational Autoencoders." Future Internet 13, no. 3 (March 16, 2021): 75. http://dx.doi.org/10.3390/fi13030075.
Yao, Heng, Jihong Guan, and Tianying Liu. "Denoising Protein–Protein interaction network via variational graph auto-encoder for protein complex detection." Journal of Bioinformatics and Computational Biology 18, no. 03 (June 2020): 2040010. http://dx.doi.org/10.1142/s0219720020400107.
Zhou, Qiang, Xinjiang Lu, Jingjing Gu, Zhe Zheng, Bo Jin, and 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, no. 8 (March 24, 2024): 9422–30. http://dx.doi.org/10.1609/aaai.v38i8.28796.
Karimi, Mostafa, Arman Hasanzadeh, and Yang Shen. "Network-principled deep generative models for designing drug combinations as graph sets." Bioinformatics 36, Supplement_1 (July 1, 2020): i445—i454. http://dx.doi.org/10.1093/bioinformatics/btaa317.
Su, Hang, Xinzheng Zhang, Yuqing Luo, Ce Zhang, Xichuan Zhou, and 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.
Xu, Lei, Leiming Xia, Shourun Pan, and Zhen Li. "Triple Generative Self-Supervised Learning Method for Molecular Property Prediction." International Journal of Molecular Sciences 25, no. 7 (March 28, 2024): 3794. http://dx.doi.org/10.3390/ijms25073794.
Du, Bing, Xiaomu Cheng, Yiping Duan, and Huansheng Ning. "fMRI Brain Decoding and Its Applications in Brain–Computer Interface: A Survey." Brain Sciences 12, no. 2 (February 7, 2022): 228. http://dx.doi.org/10.3390/brainsci12020228.
Wang, Lei, Zejian Yuan, and Badong Chen. "Learning to Generate an Unbiased Scene Graph by Using Attribute-Guided Predicate Features." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (June 26, 2023): 2581–89. http://dx.doi.org/10.1609/aaai.v37i2.25356.
Mao, Cunli, Haoyuan Liang, Zhengtao Yu, Yuxin Huang, and Junjun Guo. "A Clustering Method of Case-Involved News by Combining Topic Network and Multi-Head Attention Mechanism." Sensors 21, no. 22 (November 11, 2021): 7501. http://dx.doi.org/10.3390/s21227501.
Zhao, Mingle, Dingfu Zhou, Xibin Song, Xiuwan Chen, and Liangjun Zhang. "DiT-SLAM: Real-Time Dense Visual-Inertial SLAM with Implicit Depth Representation and Tightly-Coupled Graph Optimization." Sensors 22, no. 9 (April 28, 2022): 3389. http://dx.doi.org/10.3390/s22093389.
Li, Peng, Shufang Guo, Chenghao Zhang, Mosharaf Md Parvej, and Jing Zhang. "A Construction Method for a Dynamic Weighted Protein Network Using Multi-Level Embedding." Applied Sciences 14, no. 10 (May 11, 2024): 4090. http://dx.doi.org/10.3390/app14104090.
Zhu, Guixiang, Jie Cao, Lei Chen, Youquan Wang, Zhan Bu, Shuxin Yang, Jianqing Wu, and Zhiping Wang. "A Multi-task Graph Neural Network with Variational Graph Auto-Encoders for Session-based Travel Packages Recommendation." ACM Transactions on the Web, February 2023. http://dx.doi.org/10.1145/3577032.
Li, Dongjie, Dong Li, and Guang Lian. "Variational Graph Autoencoder with Adversarial Mutual Information Learning for Network Representation Learning." ACM Transactions on Knowledge Discovery from Data, August 22, 2022. http://dx.doi.org/10.1145/3555809.
Yuan, Wei, Shiyu Zhao, Li Wang, Lijia Cai, and Yong Zhang. "Online course evaluation model based on graph auto-encoder." Intelligent Data Analysis, March 21, 2024, 1–23. http://dx.doi.org/10.3233/ida-230557.
Li, Dongjie, Dong Li, and Guang Lian. "Variational Graph Autoencoder with Mutual Information Maximization for Graph Representations Learning." International Journal of Pattern Recognition and Artificial Intelligence, June 8, 2022. http://dx.doi.org/10.1142/s0218001422520127.
Iwata, Hiroaki, Taichi Nakai, Takuto Koyama, Shigeyuki Matsumoto, Ryosuke Kojima, and 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, November 22, 2023. http://dx.doi.org/10.1021/acs.jcim.3c01220.
Liu, Zhi, Yang Chen, Feng Xia, Jixin Bian, Bing Zhu, Guojiang Shen, and Xiangjie Kong. "TAP: Traffic Accident Profiling via Multi-task Spatio-Temporal Graph Representation Learning." ACM Transactions on Knowledge Discovery from Data, September 22, 2022. http://dx.doi.org/10.1145/3564594.
Li, Bo, Chen Peng, Zeran You, Xiaolong Zhang, and Shihua Zhang. "Single-cell RNA-sequencing data clustering using variational graph attention auto-encoder with self-supervised leaning." Briefings in Bioinformatics 24, no. 6 (September 22, 2023). http://dx.doi.org/10.1093/bib/bbad383.
Duy Nguyen, Viet Thanh, and Truong Son Hy. "Multimodal pretraining for unsupervised protein representation learning." Biology Methods and Protocols, June 18, 2024. http://dx.doi.org/10.1093/biomethods/bpae043.
Yi, Jing, and 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.
Mrabah, Nairouz, Mohamed Bouguessa, and Riadh Ksantini. "A contrastive variational graph auto-encoder for node clustering." Pattern Recognition, December 2023, 110209. http://dx.doi.org/10.1016/j.patcog.2023.110209.
Zhang, Yi, Yiwen Zhang, Dengcheng Yan, Shuiguang Deng, and Yun Yang. "Revisiting Graph-based Recommender Systems from the Perspective of Variational Auto-Encoder." ACM Transactions on Information Systems, December 2022. http://dx.doi.org/10.1145/3573385.
Zhou, Xin, and 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.
Yi, Jing, Xubin Ren, and Zhenzhong Chen. "Multi-Auxiliary Augmented Collaborative Variational Auto-encoder for Tag Recommendation." ACM Transactions on Information Systems, January 31, 2023. http://dx.doi.org/10.1145/3578932.
Chen, Han, Hanchen Wang, Hongmei Chen, Ying Zhang, Wenjie Zhang, and 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.
Zhu, Yuan, Feng Zhang, Shihua Zhang, and Ming Yi. "Predicting latent lncRNA and cancer metastatic event associations via variational graph auto-encoder." Methods, January 2023. http://dx.doi.org/10.1016/j.ymeth.2023.01.006.
Gervits, Asia, and Roded Sharan. "Predicting genetic interactions, cell line dependencies and drug sensitivities with variational graph auto-encoder." Frontiers in Bioinformatics 2 (December 2, 2022). http://dx.doi.org/10.3389/fbinf.2022.1025783.
Ding, Yulian, Xiujuan Lei, Bo Liao, and 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.
Fu, Yao, Runtao Yang, and 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.
Aftab, Rukhma, Yan Qiang, Juanjuan Zhao, Zia Urrehman, and Zijuan Zhao. "Graph Neural Network for representation learning of lung cancer." BMC Cancer 23, no. 1 (October 26, 2023). http://dx.doi.org/10.1186/s12885-023-11516-8.
Ngo, Nhat Khang, and Truong Son Hy. "Multimodal Protein Representation Learning and Target-aware Variational Auto-encoders for Protein-binding Ligand Generation." Machine Learning: Science and Technology, April 15, 2024. http://dx.doi.org/10.1088/2632-2153/ad3ee4.
Zhang, Yihao, Yuhao Wang, Wei Zhou, Pengxiang Lan, Haoran Xiang, Junlin Zhu, and Meng Yuan. "Conversational recommender based on graph sparsification and multi-hop attention." Intelligent Data Analysis, September 14, 2023, 1–21. http://dx.doi.org/10.3233/ida-230148.
Li, Yunyi, Yongjing Hao, Pengpeng Zhao, Guanfeng Liu, Yanchi Liu, Victor S. Sheng, and Xiaofang Zhou. "Edge-Enhanced Global Disentangled Graph Neural Network for Sequential Recommendation." ACM Transactions on Knowledge Discovery from Data, February 6, 2023. http://dx.doi.org/10.1145/3577928.
Peng, Lihong, Liangliang Huang, Qiongli Su, Geng Tian, Min Chen, and 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, no. 1 (November 22, 2023). http://dx.doi.org/10.1093/bib/bbad466.
Bhavna, Km, Azman Akhter, Romi Banerjee, and 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 (June 28, 2024). http://dx.doi.org/10.3389/fninf.2024.1392661.