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Continuous-time models such as Neural ODEs and Neural Flows have shownpromising results in analyzing irregularly sampled time series frequentlyencountered in electronic health records. Based on these models, time seriesare typically processed with a hybrid of an initial value problem (IVP) solverand a recurrent neural network within the variational autoencoder architecture.Sequentially solving IVPs makes such models computationally less efficient. Inthis paper, we propose to model time series purely with continuous processeswhose state evolution can be approximated directly by IVPs. This eliminates theneed for recurrent computation and enables multiple states to evolve inparallel. We further fuse the encoder and decoder with one IVP solver utilizingits invertibility, which leads to fewer parameters and faster convergence.Experiments on three real-world datasets show that the proposed method cansystematically outperform its predecessors, achieve state-of-the-art results,and have significant advantages in terms of data efficiency.

IVP-VAE: Modeling EHR Time Series with Initial Value Problem Solvers