Results 21 to 30 of about 4,069,375 (260)
Unsupervised Domain Adaptive Graph Convolutional Networks [PDF]
, 2020 Graph convolutional networks (GCNs) have achieved impressive success in many graph related analytics tasks. However, most GCNs only work in a single domain (graph) incapable of transferring knowledge from/to other domains (graphs), due to the challenges ...
Zhou, C +14 more
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Graph Convolutional Neural Networks Sensitivity under Probabilistic Error Model [PDF]
, 2022 Graph Neural Networks (GNNs), particularly Graph Convolutional Neural Networks (GCNNs), have emerged as pivotal instruments in machine learning and signal processing for processing graph-structured data.
Wang, Xinjue +2 more
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Two-way Feature Augmentation Graph Convolution Networks Algorithm [PDF]
Jisuanji kexueGraph convolutional neural network algorithms play a crucial role in the processing of graph structured data.The mainstream mode of existing graph convolutional networks is based on weighted summation of node features using Laplacian matrices,with a ...
LI Mengxi, GAO Xindan, LI Xue
doaj +1 more source
Text Classification Method Based on Graph Neural Networks [PDF]
ITM Web of ConferencesThe goal of text classification is to assign labels to text units accurately, which is a basic task in natural language processing. This technology has shown great value in many practical application scenarios, covering spam detection, emotional tendency
Gao Ruofei
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Dual graph convolutional neural network for predicting chemical networks
BMC Bioinformatics, 2020 Background Predicting of chemical compounds is one of the fundamental tasks in bioinformatics and chemoinformatics, because it contributes to various applications in metabolic engineering and drug discovery.
Shonosuke Harada +6 more
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Graph convolutional networks fusing motif-structure information
Scientific Reports, 2022 With the advent of the wave of big data, the generation of more and more graph data brings great pressure to the traditional deep learning model. The birth of graph neural network fill the gap of deep learning in graph data.
Bin Wang +4 more
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Graph Neural Networks with Convolutional ARMA Filters
, 2022 Popular graph neural networks implement convolution operations on graphs based on polynomial spectral filters. In this paper, we propose a novel graph convolutional layer inspired by the auto-regressive moving average (ARMA) filter that, compared to ...
Alippi C. +3 more
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Cloud-based video analytics using convolutional neural networks. [PDF]
, 2018 Object classification is a vital part of any video analytics system, which could aid in complex applications such as object monitoring and management.
Anjum, Ashiq +3 more
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Symbolic Hyperdimensional Vectors with Sparse Graph Convolutional Neural Networks
, 2022 In this paper, we propose a novel way of representing graphs for processing in Graph Neural Networks. We reduce the dimensionality of the input data by using Random Indexing, a Vector Symbolic Architectural framework; we implement a new trainable neural ...
Karlgren, Jussi, +3 more
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Exact combinatorial optimization with graph convolutional neural networks [PDF]
, 2019 Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm. We propose a new graph convolutional neural network model for learning branch-and-bound variable selection policies, which leverages the natural variable ...
Lodi A. +4 more
core +3 more sources