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README.md

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+<div align="center">
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+# Neural Continuous-Discrete State Space Models (NCDSSM) 
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+[![preprint](https://img.shields.io/static/v1?label=arXiv&message=2301.11308&color=B31B1B)](https://arxiv.org/abs/2301.11308)
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+[![Venue:ICML 2023](https://img.shields.io/badge/Venue-ICML%202023-007CFF)](https://icml.cc/)
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+</div>
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+<p align="center">
+  <img src="./assets/ncdssm.webp" width="30%">
+  <br />
+  <span>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.</span>
+</p>
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+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*](https://arxiv.org/abs/2301.11308). For details on how to use these checkpoints, please refer to https://github.com/clear-nus/NCDSSM.
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