  How can we effectively find the best structures in tree models? Tree models
have been favored over complex black box models in domains where
interpretability is crucial for making irreversible decisions. However,
searching for a tree structure that gives the best balance between the
performance and the interpretability remains a challenging task. In this paper,
we propose TART (Transition Matrix Representation with Transposed
Convolutions), our novel generalized tree representation for optimal structural
search. TART represents a tree model with a series of transposed convolutions
that boost the speed of inference by avoiding the creation of transition
matrices. As a result, TART allows one to search for the best tree structure
with a few design parameters, achieving higher classification accuracy than
those of baseline models in feature-based datasets.
