  The promise of ANNs to automatically discover and extract useful
features/patterns from data without dwelling on domain expertise although seems
highly promising but comes at the cost of high reliance on large amount of
accurately labeled data, which is often hard to acquire and formulate
especially in time-series domains like anomaly detection, natural disaster
management, predictive maintenance and healthcare. As these networks completely
rely on data and ignore a very important modality i.e. expert, they are unable
to harvest any benefit from the expert knowledge, which in many cases is very
useful. In this paper, we try to bridge the gap between these data driven and
expert knowledge based systems by introducing a novel framework for
incorporating expert knowledge into the network (KINN). Integrating expert
knowledge into the network has three key advantages: (a) Reduction in the
amount of data needed to train the model, (b) provision of a lower bound on the
performance of the resulting classifier by obtaining the best of both worlds,
and (c) improved convergence of model parameters (model converges in smaller
number of epochs). Although experts are extremely good in solving different
tasks, there are some trends and patterns, which are usually hidden only in the
data. Therefore, KINN employs a novel residual knowledge incorporation scheme,
which can automatically determine the quality of the predictions made by the
expert and rectify it accordingly by learning the trends/patterns from data.
Specifically, the method tries to use information contained in one modality to
complement information missed by the other. We evaluated KINN on a real world
traffic flow prediction problem. KINN significantly superseded performance of
both the expert and as well as the base network (LSTM in this case) when
evaluated in isolation, highlighting its superiority for the task.
