Unverified50% confidenceFactExact time
GAS-PFN (Graph-Aware Spatiotemporal Prior-Data Fitted Network) is a method that explicitly incorporates graph structure into the PFN prior to achieve cross-network zero-shot traffic forecasting without parameter updates.
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10/28/2026
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UnverifiedThe core idea of Prior-Fitted Networks (PFN) is to enable neural networks to directly learn to approximate the Bayesian posterior predictive distribution (PPD) by pretraining on large amounts of synthetic prior data.70% similarUnverifiedGAS-PFN uses two masked-conditioning forward passes sharing the same Transformer backbone: the first pass models the temporal dynamics specific to each sensor, the second pass focuses on spatiotemporal interactions among neighboring sensors, and the outputs of the two passes are integrated for the final prediction.67% similarUnverifiedSelectPFN is a model based on prior-data fitted networks (PFN) that can jointly perform Bayesian variable selection and prediction and is designed specifically for sparse nonlinear regression tasks; in a single forward pass it not only gives prediction results but also returns the posterior inclusion probability of each variable.67% similarUnverifiedSelectPFN can complete a prediction in about 0.04 seconds, and the fundamental reason lies in the amortized inference nature of PFN, which amortizes the per-dataset inference cost into the pretraining stage.61% similarUnverifiedEmpirical results show that PFNs are generally able to reproduce the target posterior predictive distribution well.60% similar
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