Results 1 to 10 of about 9,194 (254)
Nearest Neighbours Graph Variational AutoEncoder
Algorithms, 2023 Graphs are versatile structures for the representation of many real-world data. Deep Learning on graphs is currently able to solve a wide range of problems with excellent results.
Lorenzo Arsini +4 more
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Optimizing Variational Graph Autoencoder for Community Detection with Dual Optimization [PDF]
Entropy, 2020 Variational Graph Autoencoder (VGAE) has recently gained traction for learning representations on graphs. Its inception has allowed models to achieve state-of-the-art performance for challenging tasks such as link prediction, rating prediction, and node ...
Jun Jin Choong +2 more
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Graph Autoencoder with Preserving Node Attribute Similarity [PDF]
Entropy, 2023 The graph autoencoder (GAE) is a powerful graph representation learning tool in an unsupervised learning manner for graph data. However, most existing GAE-based methods typically focus on preserving the graph topological structure by reconstructing the ...
Mugang Lin +4 more
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Multiresolution equivariant graph variational autoencoder
Machine Learning: Science and Technology, 2023 In this paper, we propose Multiresolution Equivariant Graph Variational Autoencoders (MGVAE), the first hierarchical generative model to learn and generate graphs in a multiresolution and equivariant manner. At each resolution level, MGVAE employs higher
Truong Son Hy, Risi Kondor
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A topology-preserving dimensionality reduction method for single-cell RNA-seq data using graph autoencoder [PDF]
Scientific Reports, 2021 Dimensionality reduction is crucial for the visualization and interpretation of the high-dimensional single-cell RNA sequencing (scRNA-seq) data. However, preserving topological structure among cells to low dimensional space remains a challenge. Here, we
Zixiang Luo +3 more
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GRACE: Graph autoencoder based single-cell clustering through ensemble similarity learning. [PDF]
PLoS ONE, 2023 Recent advances in single-cell sequencing techniques have enabled gene expression profiling of individual cells in tissue samples so that it can accelerate biomedical research to develop novel therapeutic methods and effective drugs for complex disease ...
Jun Seo Ha, Hyundoo Jeong
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IRC-Safe Graph Autoencoder for Unsupervised Anomaly Detection [PDF]
Frontiers in Artificial Intelligence, 2022 Anomaly detection through employing machine learning techniques has emerged as a novel powerful tool in the search for new physics beyond the Standard Model.
Oliver Atkinson +6 more
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Importance weighted variational graph autoencoder
Complex & Intelligent SystemsVariational Graph Autoencoder (VGAE) is a widely explored model for learning the distribution of graph data. Currently, the approximate posterior distribution in VGAE-based methods is overly restrictive, leading to a significant gap between the ...
Yuhao Tao +3 more
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SGAEMDA: Predicting miRNA-Disease Associations Based on Stacked Graph Autoencoder [PDF]
Cells, 2022 Sibo Qiao +2 more
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Epitomic Variational Graph Autoencoder [PDF]
2020 25th International Conference on Pattern Recognition (ICPR), 2021 Variational autoencoder (VAE) is a widely used generative model for learning latent representations. Burda et al. in their seminal paper showed that learning capacity of VAE is limited by over-pruning. It is a phenomenon where a significant number of latent variables fail to capture any information about the input data and the corresponding hidden ...
Rayyan Ahmad Khan +2 more
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