Results 21 to 30 of about 9,194 (254)
Masked Graph Convolutional Network for Small Sample Classification of Hyperspectral Images
Remote Sensing, 2023 The deep learning method has achieved great success in hyperspectral image classification, but the lack of labeled training samples still restricts the development and application of deep learning methods.
Wenkai Liu +5 more
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On Generalization of Graph Autoencoders with Adversarial Training [PDF]
, 2021 Adversarial training is an approach for increasing model's resilience against adversarial perturbations. Such approaches have been demonstrated to result in models with feature representations that generalize better. However, limited works have been done on adversarial training of models on graph data.
Tianjin Huang +3 more
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Adversarial Attention-Based Variational Graph Autoencoder
IEEE Access, 2020 Autoencoders have been successfully used for graph embedding, and many variants have been proven to effectively express graph data and conduct graph analysis in low-dimensional space.
Ziqiang Weng, Weiyu Zhang, Wei Dou
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Multi-Prior Graph Autoencoder with Ranking-Based Band Selection for Hyperspectral Anomaly Detection
Remote Sensing, 2023 Hyperspectral anomaly detection (HAD) is an important technique used to identify objects with spectral irregularity that can contribute to object-based image analysis.
Nan Wang +5 more
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BMC Bioinformatics, 2022 Background Clinical studies show that microorganisms are closely related to human health, and the discovery of potential associations between microbes and drugs will facilitate drug research and development. However, at present, few computational methods
Yaqin Tan +6 more
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A Degeneracy Framework for Scalable Graph Autoencoders [PDF]
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019 In this paper, we present a general framework to scale graph autoencoders (AE) and graph variational autoencoders (VAE). This framework leverages graph degeneracy concepts to train models only from a dense subset of nodes instead of using the entire graph.
Guillaume Salha +3 more
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Graph Embedding Models: A Survey [PDF]
Jisuanji kexue yu tansuo, 2022 Effective graph analysis methods can reveal the intrinsic characteristics of graph data. However, graph is non-Euclidean data, which leads to high computation and space cost while applying traditional methods.
YUAN Lining, LI Xin, WANG Xiaodong, LIU Zhao
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Semi-AttentionAE: An Integrated Model for Graph Representation Learning
IEEE Access, 2021 Graph embedding learns low-dimensional vector representations which capture and preserve information in original graphs. Common shallow neural networks and deep autoencoder only use adjacency matrix as input, and usually ignore node attributes and ...
Lining Yuan +3 more
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One2Multi Graph Autoencoder for Multi-view Graph Clustering
Proceedings of The Web Conference 2020, 2020 Multi-view graph clustering, which seeks a partition of the graph with multiple views that often provide more comprehensive yet complex information, has received considerable attention in recent years. Although some efforts have been made for multi-view graph clustering and achieve decent performances, most of them employ shallow model to deal with the
Shaohua Fan +5 more
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Wasserstein Adversarially Regularized Graph Autoencoder
Neurocomputing, 2023 8 pages.
Huidong Liang, Junbin Gao
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