Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2021; 30th International C Igor Farkaš,Paolo Masulli,Stefan Wermter Conference proc

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https://doi.org/10.1007/978-3-658-18300-4ded a strong baseline for GEC and achieved excellent results by fine-tuning on a small amount of annotated data. However, due to the lack of large-scale erroneous-corrected parallel datasets, these models tend to suffer from the problem of overfitting. Previous researchers have proposed a variety of

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Rundlauffehler und Spannmittelkonstruktion,e accurate community structures in a dynamic graph. This paper introduces CmaGraph, a TriBlocks framework using an innovative deep metric learning block to measure the distances between vertices within and between communities from an evolution community detection block. A one-class anomaly detection

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Wilfried König VDI,Fritz Klocke VDIta’s strong expression ability. However, at present, graph-based methods mainly focus on node-level anomaly detection, while edge-level anomaly detection is relatively minor. Anomaly detection at the edge level can distinguish the specific edges connected to nodes as detection objects, so its resolu

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