Results 31 to 40 of about 110,849 (310)
Graph Neural Networks in Network Neuroscience
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 Noninvasive medical neuroimaging has yielded many discoveries about the brain connectivity. Several substantial techniques mapping morphological, structural and functional brain connectivities were developed to create a comprehensive road map of neuronal activities in the human brain -namely brain graph.
Alaa Bessadok +2 more
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Review of Node Classification Methods Based on Graph Convolutional Neural Networks [PDF]
Jisuanji kexueNode classification is one of the important research tasks in graph field.In recent years,with the continuous deepening of research on graph convolutional neural network,significant progress has been made in the research and application of node ...
ZHANG Liying, SUN Haihang, SUN Yufa , SHI Bingbo
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Evolutionary cellular configurations for designing feed-forward neural networks architectures [PDF]
, 2001 Proceeding of: 6th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2001 Granada, Spain, June 13–15, 2001In the recent years, the interest to develop automatic methods to determine appropriate architectures of feed-forward ...
Gutiérrez Sánchez, Germán +6 more
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Generalizable Machine Learning in Neuroscience Using Graph Neural Networks
Frontiers in Artificial Intelligence, 2021 Although a number of studies have explored deep learning in neuroscience, the application of these algorithms to neural systems on a microscopic scale, i.e. parameters relevant to lower scales of organization, remains relatively novel.
Paul Y. Wang +8 more
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Graph Condensation for Graph Neural Networks
CoRR, 2021 Given the prevalence of large-scale graphs in real-world applications, the storage and time for training neural models have raised increasing concerns. To alleviate the concerns, we propose and study the problem of graph condensation for graph neural networks (GNNs). Specifically, we aim to condense the large, original graph into a small, synthetic and
Wei Jin 0009 +5 more
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MBHAN: Motif-Based Heterogeneous Graph Attention Network
Applied Sciences, 2022 Graph neural networks are graph-based deep learning technologies that have attracted significant attention from researchers because of their powerful performance. Heterogeneous graph-based graph neural networks focus on the heterogeneity of the nodes and
Qian Hu +3 more
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Benchmarking Graph Neural Networks
J. Mach. Learn. Res., 2020 Benchmarking framework on GitHub at https://github.com/graphdeeplearning/benchmarking ...
Vijay Prakash Dwivedi +5 more
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Graph Summarization with Graph Neural Networks
CoRR, 2022 The goal of graph summarization is to represent large graphs in a structured and compact way. A graph summary based on equivalence classes preserves pre-defined features of a graph's vertex within a $k$-hop neighborhood such as the vertex labels and edge labels. Based on these neighborhood characteristics, the vertex is assigned to an equivalence class.
Maximilian Blasi +4 more
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Studying the capacity of cellular encoding to generate feedforward neural network topologies [PDF]
, 2004 Proceeding of: IEEE International Joint Conference on Neural Networks, IJCNN 2004, Budapest, 25-29 July 2004Many methods to codify artificial neural networks have been developed to avoid the disadvantages of direct encoding schema, improving the search ...
Gutiérrez Sánchez, Germán +3 more
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Graph neural networks for prediction of protein isoelectric points
, 2022 Graph neural networks were used to model protein isoelectric points. Predictions contained markedly fewer outliers (predicted with errors > 0.5 pH units) compared to tools published in the literature, despite slightly higher root-mean-squared errors ...
Tom, Brenner
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