| Presentation | 2009-07-13 Learning to imitate stochastic time series in a compositional way by chaos Jun NAMIKAWA, Jun TANI, |
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| PDF Download Page |  PDF download Page Link |
| 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 | NC |
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| Conference Date | 2009/7/6(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| Registration To | Neurocomputing (NC) |
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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) | 125 |
| Page | pp.pp.- |
| #Pages | 6 |
| Date of Issue | |