Results 41 to 50 of about 35,453 (267)
Graph Learning-Convolutional Networks
CoRR, 2018 Recently, graph Convolutional Neural Networks (graph CNNs) have been widely used for graph data representation and semi-supervised learning tasks. However, existing graph CNNs generally use a fixed graph which may be not optimal for semi-supervised learning tasks.
Bo Jiang 0002 +3 more
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Upright Adjustment With Graph Convolutional Networks
2020 IEEE International Conference on Image Processing (ICIP), 2020 We present a novel method for the upright adjustment of 360 images. Our network consists of two modules, which are a convolutional neural network (CNN) and a graph convolutional network (GCN). The input 360 images is processed with the CNN for visual feature extraction, and the extracted feature map is converted into a graph that finds a spherical ...
Raehyuk Jung, Sungmin Cho, Junseok Kwon
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Graph-Time Convolutional Neural Networks
2021 IEEE Data Science and Learning Workshop (DSLW), 2021 Spatiotemporal data can be represented as a process over a graph, which captures their spatial relationships either explicitly or implicitly. How to leverage such a structure for learning representations is one of the key challenges when working with graphs. In this paper, we represent the spatiotemporal relationships through product graphs and develop
Isufi, E. (author) +1 more
openaire +4 more sources
Integrated Spatio-Temporal Graph Neural Network for Traffic Forecasting
Applied SciencesThis research introduces integrated spatio-temporal graph convolutional networks (ISTGCN), designed to capture complex spatiotemporal traffic data patterns.
Vandana Singh +2 more
doaj +1 more source
Affinity-Point Graph Convolutional Network for 3D Point Cloud Analysis
Applied Sciences, 2022 Efficient learning of 3D shape representation from point cloud is one of the biggest requirements in 3D computer vision. In recent years, convolutional neural networks have achieved great success in 2D image representation learning.
Yang Wang, Shunping Xiao
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Single-cell classification using graph convolutional networks
BMC Bioinformatics, 2021 Background Analyzing single-cell RNA sequencing (scRNAseq) data plays an important role in understanding the intrinsic and extrinsic cellular processes in biological and biomedical research.
Tianyu Wang, Jun Bai, Sheida Nabavi
doaj +1 more source
Advanced Functional Materials, EarlyView.Thermally oxidized MoS2‐based radio‐frequency switches enable a multifunctional platform that unifies broadband RF switching and in‐memory computation. The device achieves a cutoff frequency of 33.2 THz with high energy efficiency and supports hardware‐aware signal processing.
Juho Son +5 more
wiley +1 more source
Scalable Graph Convolutional Networks With Fast Localized Spectral Filter for Directed Graphs
IEEE Access, 2020 Graph convolutional neural netwoks (GCNNs) have been emerged to handle graph-structured data in recent years. Most existing GCNNs are either spatial approaches working on neighborhood of each node, or spectral approaches based on graph Laplacian ...
Chensheng Li +4 more
doaj +1 more source
Advanced Healthcare Materials, EarlyView.Human periosteum‐derived cell spheroids bioprinted at high density within a hyaluronic acid matrix promote fusion and hypertrophic cartilage formation in vitro. Early encapsulation enhances spheroid interaction and matrix maturation, generating scalable cartilage templates intended for endochondral bone regeneration.
Ane Albillos Sanchez +6 more
wiley +1 more source
AAGCN: a graph convolutional neural network with adaptive feature and topology learning
Scientific ReportsIn recent years, there has been a growing prevalence of deep learning in various domains, owing to advancements in information technology and computing power.
Bin Wang +3 more
doaj +1 more source