Proceedings of the Annual Conference of JSAI
Online ISSN : 2758-7347
32nd (2018)
Session ID : 3Pin1-14
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Learning to resample for time-series generative model
*Takaaki KANEKOShohei OHSAWAYutaka MATSUO
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CONFERENCE PROCEEDINGS FREE ACCESS

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Abstract

Sequential Monte Carlo (SMC) is a typical sampling method that can be sampled in order from a sequential probabilistic model. However, due to degeneration of the sample, SMC may produce samples with low likelihood with a small number of particles. In our study, we focus on the fact that the same resampling targets of SMC for each sample cause degenerating samples. We want to relax this constraint, but analytically deriving asymmetric sequential resampling targets is difficult. Therefore, we expand resampling strategy of SMC asymmetrically by learning the sequential resampling target from the target of the whole series approximated to the lower bound. By this, by learning to resample, it is expected that accurate estimation of latent variable can be realized with the same particle number as SMC.

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© 2018 The Japanese Society for Artificial Intelligence
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