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GEDet: Adversarially Learned Few-shot Detection of Erroneous Nodes in Graphs.

, , , and . IEEE BigData, page 44-53. IEEE, (2020)

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GEDet: Adversarially Learned Few-shot Detection of Erroneous Nodes in Graphs., , , and . IEEE BigData, page 44-53. IEEE, (2020)Generalizing Evidence Theory to Lattices to Manage Uncertainty.. ICTAI, page 180-183. IEEE Computer Society, (1993)Camera Calibration Using Vertical Lines., , , , , , and . ACCV Workshops (1), volume 7728 of Lecture Notes in Computer Science, page 218-229. Springer, (2012)Multi-mode Study of Deep Learning Applications in Acoustic Signal Processing., , , , , and . SiPS, page 292-295. IEEE, (2018)Reform of Database and Data Warehouse Course Based on OBE-CDIO Model., , , and . SPIoT (1), volume 1282 of Advances in Intelligent Systems and Computing, page 724-730. Springer, (2020)ModsNet: Performance-aware Top-k Model Search using Exemplar Datasets., , , , , , , and . Proc. VLDB Endow., 17 (12): 4457-4460 (August 2024)A New Construction of Exact-Repair MSR Codes Using Linearly Dependent Vectors., , , and . IEEE Communications Letters, 21 (8): 1691-1694 (2017)Selecting Top-k Data Science Models by Example Dataset., , , , , , , and . CIKM, page 2686-2695. ACM, (2023)GALE: Active Adversarial Learning for Erroneous Node Detection in Graphs., , , and . ICDE, page 1705-1718. IEEE, (2023)Diversified Subgraph Query Generation with Group Fairness., , , and . WSDM, page 686-694. ACM, (2022)