Ensemble kalman variational objective: a variational inference framework for sequential variational auto-encoders
Tsuyoshi Ishizone,
Tomoyuki Higuchi,
Kazuyuki Nakamura
Abstract:Time series model inference can be divided into modeling and optimization. Sequential VAEs have been studied as a modeling technique. As an optimization technique, methods combining variational inference (VI) and sequential Monte Carlo (SMC) have been proposed; however, they have two drawbacks: less particle diversity and biased gradient estimators. This paper proposes Ensemble Kalman Variational Objective (EnKO), a VI framework with the ensemble Kalman filter, to infer latent time-series models. Our proposed … Show more
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