Transformers as Implicit State Estimators: In-Context Learning in Dynamical Systems
NeutralArtificial Intelligence
A recent study explores the use of transformers as implicit state estimators in dynamical systems, addressing the challenge of predicting system behavior from noisy past outputs. This research is significant as it builds on traditional methods like the Kalman filter, which is optimal for linear systems, and seeks to improve approaches for nonlinear systems that often rely on suboptimal heuristics. The findings could enhance predictive modeling in various engineering and scientific applications.
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