Teger is a backbone-agnostic structured uncertainty module that uses discrete Forman curvature for spatial graph rewiring inside a low-rank-plus-diagonal covariance head to mitigate over-squashing and improve residual error propagation in spatio-temporal forecasting.
Deep learning for time series forecasting: Tutorial and literature survey.ACM Computing Surveys, 55(6):1–36, December 2022
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Improving Spatio-Temporal Residual Error Propagation by Mitigating Over-Squashing
Teger is a backbone-agnostic structured uncertainty module that uses discrete Forman curvature for spatial graph rewiring inside a low-rank-plus-diagonal covariance head to mitigate over-squashing and improve residual error propagation in spatio-temporal forecasting.