Presentation | 2009-07-13 Learning to imitate stochastic time series in a compositional way by chaos Jun NAMIKAWA, Jun TANI, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | This study shows that a mixture of RNN experts model can acquire the ability to generate sequences that are combination of multiple primitive patterns by means of self-organizing chaos. By training of the model, each expert learns a primitive sequence pattern, and a gating network learns to imitate stochastic switching of the multiple primitives via a chaotic dynamics, utilizing a sensitive dependence on initial conditions. As a demonstration, we present a numerical simulation in which the model learns Markov chain switching among some Lissajous curves by a chaotic dynamics. Our analysis shows that a self-organized chaotic system can reconstruct the probability of primitive switching as observed in the training data. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | recurrent neural network / mixture of experts / maximum likelihood estimation / chaos |
Paper # | NLP2009-18,NC2009-11 |
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Conference Information | |
Committee | NLP |
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Conference Date | 2009/7/6(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Nonlinear Problems (NLP) |
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Language | ENG |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Learning to imitate stochastic time series in a compositional way by chaos |
Sub Title (in English) | |
Keyword(1) | recurrent neural network |
Keyword(2) | mixture of experts |
Keyword(3) | maximum likelihood estimation |
Keyword(4) | chaos |
1st Author's Name | Jun NAMIKAWA |
1st Author's Affiliation | Brain Science Institute, RIKEN() |
2nd Author's Name | Jun TANI |
2nd Author's Affiliation | Brain Science Institute, RIKEN |
Date | 2009-07-13 |
Paper # | NLP2009-18,NC2009-11 |
Volume (vol) | vol.109 |
Number (no) | 124 |
Page | pp.pp.- |
#Pages | 6 |
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