Feb. 14, 2024, 5:43 a.m. | Jingge Xiao Leonie Basso Wolfgang Nejdl Niloy Ganguly Sandipan Sikdar

cs.LG updates on arXiv.org arxiv.org

Continuous-time models such as Neural ODEs and Neural Flows have shown promising results in analyzing irregularly sampled time series frequently encountered in electronic health records. Based on these models, time series are typically processed with a hybrid of an initial value problem (IVP) solver and a recurrent neural network within the variational autoencoder architecture. Sequentially solving IVPs makes such models computationally less efficient. In this paper, we propose to model time series purely with continuous processes whose state evolution can …

continuous cs.ai cs.lg ehr electronic electronic health records health hybrid ivp modeling network neural network records recurrent neural network series solver time series vae value

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