Learning to imitate stochastic time series in a compositional way by chaos Accepted in Neural Networks Jun Namikawa - Hidden Markov models - Document - PDFSEARCH.IO

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	Learning to imitate stochastic time series in a compositional way by chaos Accepted in Neural Networks Jun Namikawa Add to Reading List Document Date: 2011-03-23 09:51:20 Open Document File Size: 1.001,66 KB Share Result on Facebook Company Figure 2 / / Country Japan / / / Facility RIKEN Brain Science Institute / Neural Networks Jun Namikawa Jun Tani Brain Science Institute / / IndustryTerm neural networks / Echo state networks / temporal sequence processing / recurrent network / recurrent neural network / information processing / neural network / nervous systems / expert network / echo state network / easy solution / expert networks / / Organization RIKEN Brain Science Institute / Neural Networks Jun Namikawa Jun Tani Brain Science Institute / / Position teacher / / ProgrammingLanguage E / / Technology following algorithm / simulation / neural network / / SocialTag Markov models Estimation theory Kullback–Leibler divergence Thermodynamics Recurrent neural network Long short term memory Hidden Markov model Maximum likelihood Markov chain Statistics

Learning to imitate stochastic time series in a compositional way by chaos Accepted in Neural Networks Jun NamikawaAdd to Reading List