Derives transformer-like dual-filter inference layers from first-principles optimal control on nonlinear discrete and linear Gaussian sequence models.
Dual filter: A mathematical framework for inference using transformer-like architectures
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
A neural path estimation approach learns the filtering posterior path measure for stochastic dynamical systems from noisy partial observations by solving a variational stochastic control problem based on the pathwise Zakai equation.
A control-theoretic duality unifies discrete-time linear EnKF variants by recasting minimum-variance estimation as ensemble second-order moments, with operational differences reducing to hyperparameter selection.
citing papers explorer
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Transformer-like Inference from Optimal Control
Derives transformer-like dual-filter inference layers from first-principles optimal control on nonlinear discrete and linear Gaussian sequence models.
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Pathwise Learning of Stochastic Dynamical Systems with Partial Observations
A neural path estimation approach learns the filtering posterior path measure for stochastic dynamical systems from noisy partial observations by solving a variational stochastic control problem based on the pathwise Zakai equation.
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A Unified Control Theory Derivation of Discrete-Time Linear Ensemble Kalman Filters
A control-theoretic duality unifies discrete-time linear EnKF variants by recasting minimum-variance estimation as ensemble second-order moments, with operational differences reducing to hyperparameter selection.