Search Results - (dynamic OR dynamika) graph convolutional autoencoder

Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN by 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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Recurrent graph convolutional multi-mesh autoencoder for unsteady transonic aerodynamics by 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 by 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 by 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 by 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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Dynamic graph convolutional autoencoder with node-attribute-wise attention for kidney and tumor segmentation from CT volumes by 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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Graph convolutional multi-mesh autoencoder for steady transonic aircraft aerodynamics by 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 by 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 by 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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Seismic damage identification by graph convolutional autoencoder using adjacency matrix based on structural modes by 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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Multi-modal graph convolutional network for vessel trajectory prediction based on cooperative intention enhance using conditional variational autoencoder by 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 by 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 by 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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Dynamic Semantic Compression for CNN Inference in Multi-Access Edge Computing: A Graph Reinforcement Learning-Based Autoencoder by Li, Nan, Iosifidis, Alexandros, Zhang, Qi

“…This paper studies the computational offloading of CNN inference in dynamic multi-access edge computing (MEC) networks…”
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Deep anomaly detection in horizontal axis wind turbines using Graph Convolutional Autoencoders for Multivariate Time series by 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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Mesh Convolutional Autoencoder for Semi-Regular Meshes of Different Sizes by Hahner, Sara, Garcke, Jochen

“…The analysis of deforming 3D surface meshes is accelerated by autoencoders since the low-dimensional embeddings can be used to visualize underlying dynamics…”
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Predicting potential microbe-disease associations based on heterogeneous graph attention network and deep sparse autoencoder by Wang, Bo, Zhao, Wenlong, Du, Xiaoxin, Zhang, Jianfei, Zhang, Chunyu, Wang, Liping, He, Yang

“… We propose a computational framework called graph attention convolutional deep sparse autoencoder microbe-disease association (GCDSAEMDA…”
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Graph Laplacian-Improved Convolutional Residual Autoencoder for Unsupervised Human Action and Emotion Recognition by Paoletti, Giancarlo, Beyan, Cigdem, Del Bue, Alessio

“… To lessen these shortcomings, this paper proposes a convolutional residual autoencoder…”
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A Spatio‐Temporal Enhanced Graph‐Transformer AutoEncoder embedded pose for anomaly detection by Zhu, Honglei, Wei, Pengjuan, Xu, Zhigang

“…‐based video anomaly detection in recent years. The spatio‐temporal graph convolutional network has been proven to be effective in modelling the spatio…”
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Graph Autoencoder-Based Embedded Learning in Dynamic Brain Networks for Autism Spectrum Disorder Identification by Noman, Fuad, Yap, Sin-Yee, Phan, Raphael C.-W., Ombao, Hernando, Ting, Chee-Ming

“… We exploit the graph convolutional networks (GCNs) to learn irregular structural patterns in brain FC networks and propose extensions to capture dynamic changes in network topology…”
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