# Unreasonable Effectivness of Deep Learning

arXiv: https://arxiv.org/abs/1803.10768 · HTML: https://mlsanity.com/paper/1803.10768/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2018.

## Built on by (most cited first, 1 of 1)

- [A hybrid machine learning/deep learning COVID-19 severity predictive model from CT images and clinical data](https://mlsanity.com/paper/2105.06141/) (2021)

## Abstract (arXiv)

We show how well known rules of back propagation arise from a weighted combination of finite automata. By redefining a finite automata as a predictor we combine the set of all k-state finite automata using a weighted majority algorithm. This aggregated prediction algorithm can be simplified using symmetry, and we prove the equivalence of an algorithm that does this. We demonstrate that this algorithm is equivalent to a form of a back propagation acting in a completely connected k-node neural network. Thus the use of the weighted majority algorithm allows a bound on the general performance of deep learning approaches to prediction via known results from online statistics. The presented framework opens more detailed questions about network topology; it is a bridge to the well studied techniques of semigroup theory and applying these techniques to answer what specific network topologies are capable of predicting. This informs both the design of artificial networks and the exploration of neuroscience models.