  We present results on the search for the coalescence of compact binary
mergers using convolutional neural networks and the LIGO/Virgo data,
corresponding to the O2 observation period. Two-dimensional images in time and
frequency are used as input, and two sets of neural networks are trained
separately for low mass (0.2 - 2.0 Msun) and high mass (25 - 100 Msun) compact
binary coalescence events. We explored neural networks trained with input
information from a single or a pair of interferometers, indicating that the use
of information from pairs leads to an improved performance. A scan over the
full O2 data set using the convolutional neural networks for detection
demonstrates that the performance is compatible with that from canonical
pipelines using matched filtering techniques. No additional events with
significant signal-to-noise ratio are found in the O2 data.
