Title: Gated Inference Network: Inferencing and Learning State-Space Models. Abstract: State-space models (SSMs) perform predictions by learning the underlying dynamics of observed sequence. We propose a new SSM in both high and low dimensional observation space, which utilizes Bayesian filtering-smoothing to model system’s dynamics more accurately than RNN-based SSMs and can be learned in an end-to-end manner. The designed architecture, which we call the Gated Inference Network (GIN), is able to integrate the uncertainty estimates and learn the complicated dynamics of the system that enables us to perform estimation and imputation tasks in both data presence and absence. The proposed model uses the GRU cells into its structure to complete the data flow, while avoids expensive computations and potentially unstable matrix inversions. The GIN is able to deal with any time-series data and gives us a strong robustness to handle the observational noise. In the numerical experiments, we show that the GIN reduces the
uncertainty of estimates and outperforms its counterparts , LSTMs, GRUs and variational approaches.