  Recurrent neural networks (RNNs) are a popular choice for modeling sequential
data. Modern RNN architectures assume constant time-intervals between
observations. However, in many datasets (e.g. medical records) observation
times are irregular and can carry important information. To address this
challenge, we propose continuous recurrent units (CRUs) -- a neural
architecture that can naturally handle irregular intervals between
observations. The CRU assumes a hidden state, which evolves according to a
linear stochastic differential equation and is integrated into an
encoder-decoder framework. The recursive computations of the CRU can be derived
using the continuous-discrete Kalman filter and are in closed form. The
resulting recurrent architecture has temporal continuity between hidden states
and a gating mechanism that can optimally integrate noisy observations. We
derive an efficient parameterization scheme for the CRU that leads to a fast
implementation f-CRU. We empirically study the CRU on a number of challenging
datasets and find that it can interpolate irregular time series better than
methods based on neural ordinary differential equations.
