deepexpress's Introduction

On Neural Expressivity

This repository aims to search for the neural network expressivity, how the architectural properties of a neural network - such as depth, width, layer type - could affect the resulting functions computational property.

This repository mainly rooted on the recently published article, On the Expressive Power of Deep Neural Networks (Raghu et al, 2017)

Universal Approximation Theorem
On Lower Bounds and Upper Bound

To evade the waste of computation, to find this upper and lower bound is practically important, however still not enough literature has been published.

Moreover, formalization also matters. How to define the Bounds of Neural Expressivity is still unanswered clearly.
Measure of Expressivity

Currently known measures
- Activation Pattern (Raghu et al. 2017)
  
  Using the notion of transition, where changing an input x to nearby point x + $\sigma $
- Counting Non Linear Pieces(Rincon et al. 2014)
Trajectory $x(t)$ may be more complicated, and potentially not expressible in closed form

References

Use trajectory for measure of complexity

Raghu, M., Poole, B., Kleinberg, J., Ganguli, S. & Sohl-Dickstein, J.. (2017). On the Expressive Power of Deep Neural Networks. Proceedings of the 34th International Conference on Machine Learning, in PMLR 70:2847-2854

Introducing of the concept Linear Region

Julian Rincon, Adriana Moreo, Gonzalo Alvarez: “Exotic magnetic order in the orbital-selective Mott regime of multiorbital systems”, 2014, Phys. Rev. Lett. 112, 106405 (2014); arXiv:1402.1689. DOI: 10.1103/PhysRevLett.112.106405.

This paper had revealed that:

"Given a neural network with piecewise linear activations (such as ReLU or hard tanh), the function it computes is also piecewise linear, a consequence of the fact that composing piecewise linear functions results in a piecewise linear function."