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Universal Time-Uniform Trajectory Approximation for Random Dynamical Systems with Recurrent Neural Networks
The capability of recurrent neural networks to approximate trajectories of a random dynamical system, with random inputs, on non-compact domains, and over an indefinite or infinite time horizon is considered.
Recurrent Neural Networks and Universal Approximation of Bayesian Filters
We consider the Bayesian optimal filtering problem: i. e. estimating some conditional statistics of a latent time-series signal from an observation sequence.
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