3 citations · 10 across the 16 of their papers we have counts for
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stat.ML2023
Cheap Bootstrap for Fast Uncertainty Quantification of Stochastic Gradient Descent
Henry Lam, Zitong Wang
Stochastic gradient descent (SGD) or stochastic approximation has been widely used in model training and stochastic optimization. While there is a huge literature on analyzing its…
cs.LG2023
Discriminative Graph-level Anomaly Detection via Dual-students-teacher Model
Fu Lin, Xuexiong Luo, Jia Wu +4
Different from the current node-level anomaly detection task, the goal of graph-level anomaly detection is to find abnormal graphs that significantly differ from others in a graph…
cs.LG2023★ 2 cited
Multi-representations Space Separation based Graph-level Anomaly-aware Detection
Fu Lin, Haonan Gong, Mingkang Li +3
Graph structure patterns are widely used to model different area data recently. How to detect anomalous graph information on these graph data has become a popular research problem.…