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检索条件"主题词=Variational graph auto-encoder"
15 条 记 录,以下是1-10 订阅
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Predicting latent lncRNA and cancer metastatic event associations via variational graph auto-encoder
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METHODS 2023年 第1期211卷 1-9页
作者: Zhu, Yuan Zhang, Feng Zhang, Shihua Yi, Ming China Univ Geosci Sch Automat 388 Lumo Rd Wuhan 430074 Hubei Peoples R China Hubei Key Lab Adv Control & Intelligent Automat Co 388 Lumo Rd Wuhan 430074 Hubei Peoples R China Engn Res Ctr Intelligent Technol Geoexplorat 388 Lumo Rd Wuhan 430074 Hubei Peoples R China China Univ Geosci Sch Math & Phys 388 Lumo Rd Wuhan 430074 Hubei Peoples R China Wuhan Univ Sci & Technol Coll Life Sci & Hlth 974 Heping Ave Wuhan 430081 Hubei Peoples R China
Long non-coding RNA (lncRNA) are shown to be closely associated with cancer metastatic events (CME, e.g., cancer cell invasion, intravasation, extravasation, proliferation) that collaboratively accelerate malignant ca... 详细信息
来源: 评论
LIGHT FIELD COMPRESSION VIA A variational graph auto-encoder  18
LIGHT FIELD COMPRESSION VIA A VARIATIONAL GRAPH AUTO-ENCODER
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International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)
作者: Teng, Wenjun Li, Yong Kwong, Sam City Univ Hong Kong Dept Comp Sci Kowloon 83 Tat Chee Ave Hong Kong Peoples R China
Massive light field (LF) data bring tremendous storage and transmission challenges, making the LF compression scheme highly demanded. This paper proposes a novel LF compression method via a variational graph auto-enco... 详细信息
来源: 评论
variational graph neural network with diffusion prior for link prediction
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APPLIED INTELLIGENCE 2025年 第2期55卷 1-14页
作者: Su, Hailong Li, Zhipeng Yuan, Chang-An Vladimir, F. Filaretov Huang, De-Shuang Tongji Univ Sch Elect & Informat Engn Shanghai 200082 Peoples R China Univ Sci & Technol China Hefei 230000 Anhui Peoples R China Guangxi Acad Sci Inst Big Data Nanning 530000 Guangxi Peoples R China Guangxi Acad Sci Intelligent Comp Res Ctr Nanning 530000 Guangxi Peoples R China Russian Acad Sci Inst Automat & Control Proc Far Eastern Branch Vladivostok 690041 Russia Ningbo Inst Digital Twin Eastern Inst Technol Ningbo 315201 Zhejiang Peoples R China Tongji Univ Med Innovat Ctr Inst Regenerat Med Shanghai 200123 Peoples R China Tongji Univ Shanghai East Hosp Sch Life Sci & Technol State Key Lab Cardiol Shanghai 200123 Peoples R China
Recently, graph neural networks(GNNs) has achieved tremendous success in a variety of fields. Many approaches have been proposed to address data with graph structure. However, many of these are deterministic methods, ... 详细信息
来源: 评论
End-to-end variational graph clustering with local structural preservation
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NEURAL COMPUTING & APPLICATIONS 2022年 第5期34卷 3767-3782页
作者: Guo, Lin Dai, Qun Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China
graph clustering, a basic problem in machine learning and artificial intelligence, facilitates a variety of real-world applications. How to perform a task of graph clustering, with a relatively high-quality optimizati... 详细信息
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DyVGRNN: DYnamic mixture variational graph Recurrent Neural Networks
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NEURAL NETWORKS 2023年 第1期165卷 596-610页
作者: Niknam, Ghazaleh Molaei, Soheila Zare, Hadi Pan, Shirui Jalili, Mahdi Zhu, Tingting Clifton, David Univ Tehran Dept Data Sci & Technol Tehran Iran Univ Oxford Dept Engn Sci Oxford England Griffith Univ Sch Informat & Commun Technol Nathan Qld Australia RMIT Univ Sch Engn Bundoora Vic Australia Oxford Suzhou Ctr Adv Res OSCAR Suzhou Peoples R China
Although graph representation learning has been studied extensively in static graph settings, dynamic graphs are less investigated in this context. This paper proposes a novel integrated variational framework called D... 详细信息
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graph Convolutional auto-encoders for Predicting Novel lncRNA-Disease Associations
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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022年 第4期19卷 2264-2271页
作者: Silva, Ana B. O., V Spinosa, E. J. Univ Fed Parana BR-80060000 Curitiba Parana Brazil
LncRNAs are intermediate molecules that participate in the most diverse biological processes in humans, such as gene expression control and X-chromosome inactivation. Numerous researches have associated lncRNAs with a... 详细信息
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variational graph autoencoder with Adversarial Mutual Information Learning for Network Representation Learning
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ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 2022年 第3期17卷 1-18页
作者: Li, Dongjie Li, Dong Lian, Guang South China Univ Technol Guangzhou Peoples R China
With the success of graph Neural Network (GNN) in network data, some GNN-based representation learning methods for networks have emerged recently. variational graph autoencoder (VGAE) is a basic GNN framework for netw... 详细信息
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Predicting potential interactions between lncRNAs and proteins via combined graph auto -encoder methods
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BRIEFINGS IN BIOINFORMATICS 2023年 第1期24卷 bbac527-bbac527页
作者: Zhao, Jingxuan Sun, Jianqiang Shuai, Stella C. Zhao, Qi Shuai, Jianwei Univ Sci & Technol Liaoning Anshan Peoples R China Linyi Univ Linyi Peoples R China Northwestern Univ Evanston IL USA Xiamen Univ Dept Phys Xiamen Peoples R China
Long noncoding RNA (lncRNA) is a kind of noncoding RNA with a length of more than 200 nucleotide units. Numerous research studies have proven that although lncRNAs cannot be directly translated into proteins, lncRNAs ... 详细信息
来源: 评论
variational graph Author Topic Modeling  22
Variational Graph Author Topic Modeling
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28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KKD)
作者: Zhang, Delvin Ce Lauw, Hady W. Singapore Management Univ Singapore Singapore
While variational graphauto-encoder (VGAE) has presented promising ability to learn representations for documents, most existing VGAE methods do not model a latent topic structure and therefore lack semantic interpret... 详细信息
来源: 评论
DefenseVGAE: Defending Against Adversarial Attacks on graph Data via a variational graph autoencoder  20th
DefenseVGAE: Defending Against Adversarial Attacks on Graph ...
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20th International Conference on Intelligent Computing (ICIC)
作者: Zhang, Ao Ma, Jinwen Peking Univ Sch Math Sci Dept Informat & Computat Sci Beijing 100871 Peoples R China
graph neural networks (GNNs) achieve remarkable performances for the tasks on graph data. However, recent studies uncover that they are extremely vulnerable to adversarial structural perturbations, leading to their ou... 详细信息
来源: 评论