Introduces CASSM, a computation-aware state-space model extending Kalman filtering with model selection for scale-imbalanced neural recordings, claiming competitive performance with deep networks and improved uncertainty calibration.
The geometry of low-rank Kalman filters
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abstract
An important property of the Kalman filter is that the underlying Riccati flow is a contraction for the natural metric of the cone of symmetric positive definite matrices. The present paper studies the geometry of a low-rank version of the Kalman filter. The underlying Riccati flow evolves on the manifold of fixed rank symmetric positive semidefinite matrices. Contraction properties of the low-rank flow are studied by means of a suitable metric recently introduced by the authors.
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stat.ML 1years
2026 1verdicts
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Computation-Aware Kalman Filtering with Model Selection for Neural Dynamics
Introduces CASSM, a computation-aware state-space model extending Kalman filtering with model selection for scale-imbalanced neural recordings, claiming competitive performance with deep networks and improved uncertainty calibration.