A few-shot graph-pretraining pipeline, built from subgraph sampling and a hybrid graph transformer, predicts parasitic coupling capacitance on unseen AMS circuits with substantially lower error than prior graph baselines.
Recipe for a General, Powerful, Scalable Graph Trans- former,
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Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction
A few-shot graph-pretraining pipeline, built from subgraph sampling and a hybrid graph transformer, predicts parasitic coupling capacitance on unseen AMS circuits with substantially lower error than prior graph baselines.