Graph Convolutional Networks (GCNs) are widely applied for spatial domain identification in spatial transcriptomics (ST), where node representations are learned by aggregating information from ...
Abstract: Timely detection and accurate diagnosis of faults in technological processes can significantly reduce production costs in manufacturing. Modern industrial equipment, equipped with numerous ...
Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Cory Benfield discusses the evolution of ...
Abstract: Graph Convolutional Networks (GCNs) have achieved notable success in skeleton-based action recognition. However, research scholars face issues such as low discriminability between actions ...
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We publish the best academic work (that's too often lost to peer reviews & the TA's desk) to the global tech community byEScholar: Electronic Academic Papers for Scholars@escholar byEScholar: ...
An unsophisticated graph library that supports creating directed or undirected graphs with custom weights. There is the option to choose between an adjacency matrix or list. This application was built ...
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