[3Pin1] インタラクティブ(1)

[3Pin1-14] Learning to resample for time-series generative model

〇Takaaki Kaneko¹, Shohei Ohsawa¹, Yutaka Matsuo¹ (1. Department of Technology Management for Innovation, The University of Tokyo)

Keywords:sequential monte carlo

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.