  We introduce a novel methodology for the operation of an early %warning alert
system for gravitational waves. It is based on short convolutional neural
networks. We focus on compact binary coalescences, for light, intermediate and
heavy binary-neutron-star systems. The signals are 1-dimensional time series
$-$ the whitened time-strain $-$ injected in Gaussian noise built from the
power-spectral density of the LIGO detectors at design sensitivity. We build
short 1-dimensional convolutional neural networks to detect these types of
events by training them on part of the early inspiral. We show that such
networks are able to retrieve these signals from a small portion of the
waveform.
