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Understanding Dropout for Graph Neural Networks.

, , , , , and . WWW (Companion Volume), page 1128-1138. ACM, (2022)

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Understanding Dropout for Graph Neural Networks., , , , , and . WWW (Companion Volume), page 1128-1138. ACM, (2022)Understanding Adversarial Examples Through Deep Neural Network's Classification Boundary and Uncertainty Regions., , and . AISafety@IJCAI, volume 3215 of CEUR Workshop Proceedings, CEUR-WS.org, (2022)GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction., , , , , , and . WSDM, page 576-585. ACM, (2024)Fast Semantic Preserving Hashing for Large-Scale Cross-Modal Retrieval., , , , , and . ICDM, page 1348-1353. IEEE, (2019)Efficient Human Motion Transition via Hybrid Deep Neural Network and Reliable Motion Graph Mining., , , , and . CCCV (1), volume 771 of Communications in Computer and Information Science, page 717-728. Springer, (2017)Automatic Character Motion Style Transfer via Autoencoder Generative Model and Spatio-Temporal Correlation Mining., , , , and . CCCV (1), volume 771 of Communications in Computer and Information Science, page 705-716. Springer, (2017)Automatic mitral valve leaflet tracking in Echocardiography via constrained outlier pursuit and region-scalable active contours., , , and . Neurocomputing, (2014)Towards Efficient Cross-Modal Anomaly Detection Using Triple-Adaptive Network and Bi-Quintuple Contrastive Learning., , , , , and . IEEE Trans. Emerg. Top. Comput. Intell., 8 (1): 697-709 (February 2024)An Impression-Based Strategy for Defending Reputation Attacks in Multi-agent Reputation System., , , and . ISCID (2), page 383-388. IEEE, (2016)Constructing the Internet Behavior Ontology: Projection from Psychological Phenomena with Qualitative and Quantitative Methods., , , , , , , , , and . AMT, volume 6890 of Lecture Notes in Computer Science, page 123-128. Springer, (2011)