  Data are often sampled irregularly in time. Dealing with this using Recurrent
Neural Networks (RNNs) traditionally involved ignoring the fact, feeding the
time differences as additional inputs, or resampling the data. All these
methods have their shortcomings. We propose an elegant straightforward
alternative approach where instead the RNN is in effect resampled in time to
match the time of the data or the task at hand. We use Echo State Network (ESN)
and Gated Recurrent Unit (GRU) as the basis for our solution. Such RNNs can be
seen as discretizations of continuous-time dynamical systems, which gives a
solid theoretical ground to our approach. Our Task-Synchronized ESN (TSESN) and
GRU (TSGRU) models allow for a direct model time setting and require no
additional training, parameter tuning, or computation (solving differential
equations or interpolating data) compared to their regular counterparts, thus
retaining their original efficiency. We confirm empirically that our models can
effectively compensate for the time-non-uniformity of the data and demonstrate
that they compare favorably to data resampling, classical RNN methods, and
alternative RNN models proposed to deal with time irregularities on several
real-world nonuniform-time datasets. We open-source the code at
https://github.com/oshapio/task-synchronized-RNNs .
