Interesting—I will have a read when I have time to read and check the math/logic. Thanks!

I do think I am allowed to remain skeptical for now because this was just posted as a pre-print with a single author a month ago and has not been vetted by the community.

Besides, if there is an equivalence between recurrent neural networks, convolutional neural networks, fully connected networks, policies learned with deep reinforcement learning, and all of this regardless of the architecture, how the network is trained, and so on, and there always exists a decision tree that is equivalent, then I would say:

1. Very interesting

2. Decision trees are then more flexible and powerful than we give them credit for, not NNs are less flexible and less powerful than they have been proven to be.

3. What is it about decision trees that makes people not use them in practice for anything too complicated on full motion video, etc? How does one construct the decision tree “from scratch” via training except by training the NN first, then building a decision tree that represents the NN? I wouldn’t say “they’re the same” from an engineering and practical point of view if one can be trained efficiently and the other cannot, but can only be built once the trained NN already exists.