  The goal of the Search for Extraterrestrial Intelligence (SETI) is to
quantify the prevalence of technological life beyond Earth via their
"technosignatures". One theorized technosignature is narrowband Doppler
drifting radio signals. The principal challenge in conducting SETI in the radio
domain is developing a generalized technique to reject human radio frequency
interference (RFI). Here, we present the most comprehensive deep-learning based
technosignature search to date, returning 8 promising ETI signals of interest
for re-observation as part of the Breakthrough Listen initiative. The search
comprises 820 unique targets observed with the Robert C. Byrd Green Bank
Telescope, totaling over 480, hr of on-sky data. We implement a novel
beta-Convolutional Variational Autoencoder to identify technosignature
candidates in a semi-unsupervised manner while keeping the false positive rate
manageably low. This new approach presents itself as a leading solution in
accelerating SETI and other transient research into the age of data-driven
astronomy.
