  We present the results on the search for the coalescence of compact binary
mergers with very asymmetric mass configurations using convolutional neural
networks and the LIGO/Virgo data for the O3 observation period. Two-dimensional
images in time and frequency are used as input. Masses in the range between
0.01 Msun and 20 Msun are considered. We explore neural networks trained with
input information from a single interferometer, pairs of interferometers, or
all three interferometers together, indicating that the use of the maximum
information available leads to an improved performance. A scan over the O3 data
set using the convolutional neural networks for detection results into no
significant excess from an only-noise hypothesis. The results are translated
into 90% confidence level upper limits on the merger rate as a function of the
mass parameters of the binary system.
