I think your work in this paper is pretty much entirely subsumed by the following work showing that neural networks with piecewise linear activations are equivalent to max-affine spline operators: https://arxiv.org/abs/1805.06576
They seem to cover everything you do and more, although they don’t take a specifically tree-oriented viewpoint. Unfortunately, like many of the others in this thread, I don’t find results like this particularly compelling.
I think your work in this paper is pretty much entirely subsumed by the following work showing that neural networks with piecewise linear activations are equivalent to max-affine spline operators: https://arxiv.org/abs/1805.06576
They seem to cover everything you do and more, although they don’t take a specifically tree-oriented viewpoint. Unfortunately, like many of the others in this thread, I don’t find results like this particularly compelling.
This thread might be fun for you, where Reddit talks about some papers that draw connections between NNs and decision trees. https://www.reddit.com/r/MachineLearning/comments/y2pi2a/r_neural_networks_are_decision_trees/
In particular, look for the comment that goes
I think your work in this paper is pretty much entirely subsumed by the following work showing that neural networks with piecewise linear activations are equivalent to max-affine spline operators: https://arxiv.org/abs/1805.06576
They seem to cover everything you do and more, although they don’t take a specifically tree-oriented viewpoint. Unfortunately, like many of the others in this thread, I don’t find results like this particularly compelling.