GRAD fuses spatial, wavelet, and Fourier visual features with kinematic robot data through graph attention and adversarial alignment, and reports top accuracy plus improved corruption tolerance on two surgical gesture datasets.
Are Graph Neural Networks Miscalibrated?
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abstract
Graph Neural Networks (GNNs) have proven to be successful in many classification tasks, outperforming previous state-of-the-art methods in terms of accuracy. However, accuracy alone is not enough for high-stakes decision making. Decision makers want to know the likelihood that a specific GNN prediction is correct. For this purpose, obtaining calibrated models is essential. In this work, we perform an empirical evaluation of the calibration of state-of-the-art GNNs on multiple datasets. Our experiments show that GNNs can be calibrated in some datasets but also badly miscalibrated in others, and that state-of-the-art calibration methods are helpful but do not fix the problem.
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cs.CV 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
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Multimodal Graph Representation Learning for Robust Surgical Workflow Recognition with Adversarial Feature Disentanglement
GRAD fuses spatial, wavelet, and Fourier visual features with kinematic robot data through graph attention and adversarial alignment, and reports top accuracy plus improved corruption tolerance on two surgical gesture datasets.