MARKOV RECURRENT NEURAL NETWORKS

@article{Kuo2018MARKOVRN,
  title={MARKOV RECURRENT NEURAL NETWORKS},
  author={Che-Yu Kuo and Jen-Tzung Chien},
  journal={2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP)},
  year={2018},
  pages={1-6}
}
Deep learning has achieved great success in many real-world applications. For speech and language processing, recurrent neural networks are learned to characterize sequential patterns and extract the temporal information based on dynamic states which are evolved through time and stored as an internal memory. Traditionally, simple transition function using input-to-hidden and hidden-to-hidden weights is insufficient. To strengthen the learning capability, it is crucial to explore the diversity… CONTINUE READING

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