1 code implementation • 27 Oct 2022 • Hans Hao-Hsun Hsu, Yuesong Shen, Daniel Cremers
Current graph neural networks (GNNs) that tackle node classification on graphs tend to only focus on nodewise scores and are solely evaluated by nodewise metrics.
1 code implementation • 12 Oct 2022 • Hans Hao-Hsun Hsu, Yuesong Shen, Christian Tomani, Daniel Cremers
Furthermore, based on the insights from this study, we design a novel calibration method named Graph Attention Temperature Scaling (GATS), which is tailored for calibrating graph neural networks.
no code implementations • 27 Nov 2021 • Hans Hao-Hsun Hsu, Jiawen Xu, Ravi Sama, Matthias Kovatsch
A contour-based anomaly detector can then map the reconstruction error matrix to an anomaly score to identify faulty antenna arrays and increase the classification F-measure (F-M) by up to 46%.