  Turing machine and decision tree have developed independently for a long
time. With the recent development of differentiable models, there is an
intersection between them. Neural turing machine(NTM) opens door for the memory
network. It use differentiable attention mechanism to read/write external
memory bank. Differentiable forest brings differentiable properties to
classical decision tree. In this short note, we show the deep connection
between these two models. That is: differentiable forest is a special case of
NTM. Differentiable forest is actually decision tree based neural turing
machine. Based on this deep connection, we propose a response augmented
differential forest (RaDF). The controller of RaDF is differentiable forest,
the external memory of RaDF are response vectors which would be read/write by
leaf nodes.
