An input-output variant of Neural Jump ODEs is proven to converge to the L2-optimal conditional expectation for online filtering and classification with irregularly sampled, partially observed data.
Extending path-dependent NJ - ODE s to noisy observations and a dependent observation framework
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Nonparametric Filtering, Estimation and Classification using Neural Jump ODEs
An input-output variant of Neural Jump ODEs is proven to converge to the L2-optimal conditional expectation for online filtering and classification with irregularly sampled, partially observed data.