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- W1615677613 abstract "A stochastic model is established for fully-connected recurrent neural networks with sigmoid units based on Gibbs distributions. The EM (expectation-maximization) algorithm with a mean field approximation is then applied to train recurrent networks through hidden state estimation. The resulting EM-based algorithm, which reduces training the original recurrent network to training a set of individual feedforward neurons, simplifies the original training process and reduces the training time." @default.
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- W1615677613 date "2002-11-19" @default.
- W1615677613 modified "2023-09-26" @default.
- W1615677613 title "An EM-based algorithm for recurrent neural networks" @default.
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- W1615677613 doi "https://doi.org/10.1109/isit.1995.531524" @default.
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