  Differentiable forest is an ensemble of decision trees with full
differentiability. Its simple tree structure is easy to use and explain. With
full differentiability, it would be trained in the end-to-end learning
framework with gradient-based optimization method. In this paper, we propose
tree attention block(TAB) in the framework of differentiable forest. TAB block
has two operations, squeeze and regulate. The squeeze operation would extract
the characteristic of each tree. The regulate operation would learn nonlinear
relations between these trees. So TAB block would learn the importance of each
tree and adjust its weight to improve accuracy. Our experiment on large tabular
dataset shows attention augmented differentiable forest would get comparable
accuracy with gradient boosted decision trees(GBDT), which is the
state-of-the-art algorithm for tabular datasets. And on some datasets, our
model has higher accuracy than best GBDT libs (LightGBM, Catboost, and
XGBoost). Differentiable forest model supports batch training and batch size is
much smaller than the size of training set. So on larger data sets, its memory
usage is much lower than GBDT model. The source codes are available at
https://github.com/closest-git/QuantumForest.
