  The detection of gravitational waves has revolutionized our understanding of
the universe, offering unprecedented insights into its dynamics. A major goal
of gravitational wave data analysis is to speed up the detection and parameter
estimation process using machine learning techniques, in light of an
anticipated surge in detected events that would render traditional methods
impractical. Here, we present the first detections of new gravitational-wave
candidate events in data from a network of interferometric detectors enabled by
machine learning. We discuss several new enhancements of our ResNet-based deep
learning code, AresGW, that increased its sensitivity, including a new
hierarchical classification of triggers, based on different noise and frequency
filters. The enhancements resulted in a significant reduction in the false
alarm rate, allowing AresGW to surpass traditional pipelines in the number of
detected events in its effective training range (single source masses between 7
and 50 solar masses and source chirp masses between 10 and 40 solar masses),
when the new detections are included. We calculate the astrophysical
significance of events detected with AresGW using a logarithmic ranking
statistic and injections into O3 data. Furthermore, we present spectrograms,
parameter estimation, and reconstruction in the time domain for our new
candidate events and discuss the distribution of their properties. In addition,
the AresGW code exhibited very good performance when tested across various
two-detector setups and on observational data from the O1 and O2 observing
periods. Our findings underscore the remarkable potential of AresGW as a fast
and sensitive detection algorithm for gravitational-wave astronomy, paving the
way for a larger number of future discoveries.
