State-space unlearning for land surface forecasting reduces non-stationary bias from unrecorded confounding events in Mamba-family models.
Read the original at arxiv.org→arXiv:2610.02248v1 Announce Type: new Abstract: Operational land surface forecasting systems built on Mamba-family Structured State Space Models absorb non-stationary confounding events (unrecorded irrigation booms,...
Original headline: "State-Space Unlearning for Non-Stationary Bias in Land Surface Forecasting"
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- Oct 5, 04:00 UTC arXiv cs.LG lead source State-Space Unlearning for Non-Stationary Bias in Land Surface Forecasting