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Approximating Real-Time Recurrent Learning with Random Kronecker Factors
Asier Mujika, Florian Meier, Angelika Steger
Wealso confirm these theoretical results experimentally. Further,we showempirically thattheKF-RTRLalgorithm captures long-term dependencies and almost matches the performance of TBPTT on real world tasks by trainingRecurrent Highway Networks on a synthetic string memorization task and onthe Penn TreeBank task, respectively.
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