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license: mit
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<div align="center">
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# Neural Continuous-Discrete State Space Models (NCDSSM)
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[](https://arxiv.org/abs/2301.11308)
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[](https://opensource.org/licenses/MIT)
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[](https://icml.cc/)
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</div>
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<p align="center">
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<img src="assets/ncdssm.webp" width="30%">
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<br />
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<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>
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</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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license: mit
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# Neural Continuous-Discrete State Space Models (NCDSSM)
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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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