  A new implementation of an adiabatically-trained ensemble model is derived
that shows significant improvements over classical methods. In particular,
empirical results of this new algorithm show that it offers not just higher
performance, but also more stability with less classifiers, an attribute that
is critically important in areas like explainability and speed-of-inference. In
all, the empirical analysis displays that the algorithm can provide an increase
in performance on unseen data by strengthening stability of the statistical
model through further minimizing and balancing variance and bias, while
decreasing the time to convergence over its predecessors.
