Suchergebnisse - Dynamic graph convolutional autoencoder

Dynamic graph convolutional autoencoder with node-attribute-wise attention for kidney and tumor segmentation from CT volumes von Xuan, Ping, Cui, Hui, Zhang, Hongda, Zhang, Tiangang, Wang, Linlin, Nakaguchi, Toshiya, Duh, Henry B.L.

“… We propose a novel dynamic graph convolution (DGC) autoencoder with node-attribute-wise attention (NodeAttri-Attention …”
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Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN von Zeng, Ming, Wang, Min, Xie, Fuqiang, Ji, Zhiwei

“… of network’s representation capabilities. Results In this paper, we propose a graph convolutional autoencoder model, named DDGAE, for DTIs prediction …”
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Dynamic graph convolutional autoencoder with node-attribute-wise attention for kidney and tumor segmentation from CT volumes von Ping Xuan, Hui Cui, Hongda Zhang, Tiangang Zhang, Linlin Wang, Toshiya Nakaguchi, Henry B.L. Duh

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GDGC-AE: A New Approach to Mechanical Anomaly Detection Based on Graph Convolutional Networks and Autoencoders von Zhang, Mingzhe, Su, Zhengchang, Hao, Pengyuan, Lin, Zesheng, Wang, Huaqing, Song, Liuyang

“… In this paper, a global dynamic graph convolutional autoencoder (GDGC-AE) model based on Chebyshev convolution is proposed to cope with the above problems …”
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Seismic damage identification by graph convolutional autoencoder using adjacency matrix based on structural modes von Kim, Minkyu, Song, Junho

“… ‐time damage identification by a graph convolutional autoencoder (GCAE) based on seismic responses of the structural system …”
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Enhancing microbe-disease association prediction via multi-view graph convolution and latent feature learning von Wang, Bo, Wu, Peilong, Du, Xiaoxin, Zhang, Chunyu, Fu, Shanshan, Sun, Tang, Yang, Xue

“… MVGCVAE is the first model to synergistically integrate multi-view graph convolutional networks (GCNs …”
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Deep anomaly detection in horizontal axis wind turbines using Graph Convolutional Autoencoders for Multivariate Time series von Miele, Eric Stefan, Bonacina, Fabrizio, Corsini, Alessandro

“… We introduce a promising neural architecture, namely a Graph Convolutional Autoencoder for Multivariate Time series, to model the sensor network as a dynamical functional graph …”
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A Novel Unsupervised Structural Damage Detection Method Based on TCN-GAT Autoencoder von Ni, Yanchun, Jin, Qiyuan, Hu, Rui

“… This paper proposes an autoencoder model integrating Temporal Convolutional Networks (TCN) and Graph Attention Networks (GAT …”
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Recurrent graph convolutional multi-mesh autoencoder for unsteady transonic aerodynamics von Massegur, David, Da Ronch, Andrea

“… This work presents a geometric-deep-learning multi-mesh autoencoder framework to predict the spatial and temporal evolution of aerodynamic loads for a finite-span wing undergoing different types of motion …”
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Convolutional Graph Autoencoder: A Generative Deep Neural Network for Probabilistic Spatio-Temporal Solar Irradiance Forecasting von Khodayar, Mahdi, Mohammadi, Saeed, Khodayar, Mohammad E., Wang, Jianhui, Liu, Guangyi

“… This probabilistic data generation model, i.e., convolutional graph autoencoder (CGAE), is devised based on the localized first-order approximation …”
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Predicting transonic flowfields in non–homogeneous unstructured grids using autoencoder graph convolutional networks von Immordino, Gabriele, Vaiuso, Andrea, Da Ronch, Andrea, Righi, Marcello

“… Our approach leverages geometric deep learning, specifically through the use of an autoencoder architecture built on graph convolutional networks …”
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iCircDA-NEAE: Accelerated attribute network embedding and dynamic convolutional autoencoder for circRNA-disease associations prediction von Yuan, Lin, Zhao, Jiawang, Shen, Zhen, Zhang, Qinhu, Geng, Yushui, Zheng, Chun-Hou, Huang, De-Shuang

“… Accumulating evidence suggests that circRNAs play crucial roles in human diseases. CircRNA-disease association prediction is extremely helpful in understanding …”
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AI-based clinical assessment of optic nerve head robustness superseding biomechanical testing von Braeu, Fabian A, Chuangsuwanich, Thanadet, Tun, Tin A, Perera, Shamira, Husain, Rahat, Thiery, Alexandre H, Aung, Tin, Barbastathis, George, Girard, Michaël J A

“… Background/aimsTo use artificial intelligence (AI) to: (1) exploit biomechanical knowledge of the optic nerve head (ONH) from a relatively large population; …”
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Graph convolutional multi-mesh autoencoder for steady transonic aircraft aerodynamics von Massegur, David, Da Ronch, Andrea

“… Calculating aerodynamic loads around an aircraft using computational fluid dynamics …”
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Graph autoencoder with mirror temporal convolutional networks for traffic anomaly detection von Ren, Zhiyu, Li, Xiaojie, Peng, Jing, Chen, Ken, Tan, Qushan, Wu, Xi, Shi, Canghong

“… In this paper, we propose a mirror temporal graph autoencoder (MTGAE) framework to explore anomalies and capture unseen nodes and the spatiotemporal correlation between nodes in the traffic network …”
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Graph-informed convolutional autoencoder to classify brain responses during sleep von Zakeri, Sahar, Makouei, Somayeh, Danishvar, Sebelan

“… Automated machine-learning algorithms that analyze biomedical signals have been used to identify sleep patterns and health issues. However, their performance …”
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Multi-modal graph convolutional network for vessel trajectory prediction based on cooperative intention enhance using conditional variational autoencoder von Jiang, Junhao, Zuo, Yi, Li, Zhiyuan

“… of trajectory prediction. To address these challenges, we propose a cooperative intention enhance multi-modal graph convolutional network (CIE-MGCN …”
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ERA-WGAT: Edge-enhanced residual autoencoder with a window-based graph attention convolutional network for low-dose CT denoising von Liu, Han, Liao, Peixi, Chen, Hu, Zhang, Yi

“… and a window-based graph attention convolutional network that combines static and dynamic attention modules to explore non-local self-similarity …”
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Spatio-temporal graph convolutional autoencoder for transonic wing pressure distribution forecasting von Immordino, Gabriele, Vaiuso, Andrea, Da Ronch, Andrea, Righi, Marcello

“… This study presents a framework for predicting unsteady transonic wing pressure distributions due to pitch and plunge movement, integrating an autoencoder architecture with graph convolutional …”
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PT-TDGCN: Pre-Trained Trend-Aware Dynamic Graph Convolutional Network for Traffic Flow Prediction von Yang, Hanqing, Wei, Sen, Wang, Yuanqing

“… To address these issues, we propose the Pre-trained Trend-aware Dynamic Graph Convolutional Network (PT-TDGCN …”
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