Presentation | 2009-01-19 Experimental Study of Bayesian Learning using Langevin Equation in Singular Learing Machines Taruhi IWAGAKI, Sumio WATANABE, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Langevin equation implies an algorithm that could make samples from the stationary distribution of a biased random walk equivalent to the posterior distribution of Bayesian learning. Sampling with Langevin equation uses gradient information of the target distribution, therefore it is expected to be efficient than Metropolis algorithm especially in wide parameter space of singular learning machines like neural networks. In this paper, we will discuss experimental results and generalization errors of both Langevin algorithm and Metropolis algorithm for neural networks with practical dimension. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Singular Learning Machines / Langevein Equation / Fokker-Planck Equation / Baysian Learning |
Paper # | NC2008-88 |
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Committee | NC |
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Conference Date | 2009/1/12(1days) |
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Registration To | Neurocomputing (NC) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Experimental Study of Bayesian Learning using Langevin Equation in Singular Learing Machines |
Sub Title (in English) | |
Keyword(1) | Singular Learning Machines |
Keyword(2) | Langevein Equation |
Keyword(3) | Fokker-Planck Equation |
Keyword(4) | Baysian Learning |
1st Author's Name | Taruhi IWAGAKI |
1st Author's Affiliation | Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology() |
2nd Author's Name | Sumio WATANABE |
2nd Author's Affiliation | Precision and Intelligence Laboratory, Tokyo Institute of Technology |
Date | 2009-01-19 |
Paper # | NC2008-88 |
Volume (vol) | vol.108 |
Number (no) | 383 |
Page | pp.pp.- |
#Pages | 6 |
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