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license: mit
Fig 1. (Top) Generative model of Neural Continuous-Discrete State Space Model. (Bottom) Amortized inference for auxiliary variables and continuous-discrete Bayesian inference for states.
This repository contains pretrained checkpoints for reproducing the experiments presented in the ICML 2023 paper Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series. For details on how to use these checkpoints, please refer to https://github.com/clear-nus/NCDSSM.