Abstract
Kalman filter based training algorithms for recurrent neural networks provide a clever alternative to the standard backpropagation in time. However, these algorithms do not take into account the optimization of the hidden state variables of the recurrent network. In addition, their formulation requires Jacobian evaluations over the entire network, adding to their computational complexity. We propose a spatial-temporal extended Kalman filter algorithm for training recurrent neural network weights and internal states. This new formulation also reduces the computational complexity of Jacobian evaluations drastically by decoupling the gradients of each layer. Monte Carlo comparisons with backpropagation through time point out the robust and fast convergence of the algorithm.
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