Distributed Representations, Simple Recurrent Networks, And Grammatical Structure
Machine Learning - Connectionist approaches to language learning
A learning algorithm for continually running fully recurrent neural networks
Neural Computation
Neural Network Training with Extended Kalman Filter Using Graphics Processing Unit
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
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Recurrent neural networks are popular tools used for modeling time series. Common gradient-based algorithms are frequently used for training recurrent neural networks. On the other side approaches based on the Kalman filtration are considered to be the most appropriate general-purpose training algorithms with respect to the modeling accuracy. Their main drawbacks are high computational requirements and difficult implementation. In this work we first provide clear description of the training algorithm using simple pseudo-language. Problem with high computational requirements is addresses by performing calculation on Multicore Processor and CUDA-enabled graphic processor unit. We show that important execution time reduction can be achieved by performing computation on manycore graphic processor unit.