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This paper informs a statistical readership about Artificial Neural Networks (ANNs), points out some of the links with statistical methodology and ...
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Mar 31, 2021 · This review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field.
Graph neural networks (GNNs) are neural models that capture the dependence of graphs via message passing between the nodes of graphs.
Sep 25, 2023 · This passage offers an assessment of Neural Networks and their pragmatic uses in real-world situations.
This article reviews current advances and developments in neural networks. This requires recalling some of the earlier work in the field.
May 18, 2023 · This paper presents a comprehensive review of current brain-inspired learning representations in artificial neural networks. We investigate the ...
This review provides a comprehensive overview of neural networks, tracing their evolution, architecture, training methodologies, and diverse applications. We ...
May 9, 2024 · This paper presents a comprehensive review of current brain-inspired learning representations in artificial neural networks.
Neural Networks provides a forum for developing and nurturing an international community of scholars and practitioners who are interested in all aspects of
Jan 16, 2024 · This paper identifies nodes and edges within specific applications, such as text, entities, and relations, to create graph structures.
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