# Topology of Learning in Artificial Neural Networks

## TL;DR

This paper investigates the topological structure of neural network weights during training using topological data analysis, revealing different geometric patterns depending on initialization, and linking these structures to learning dynamics.

## Contribution

It introduces a topological perspective to analyze neural network training, uncovering how weight structures evolve and relate to learning processes.

## Key findings

- Zero initialization leads to tree-like weight trajectories.
- Random small initialization results in smooth two-dimensional weight surfaces.
- Learning surfaces encode important factors of variation.

## Abstract

Understanding how neural networks learn remains one of the central challenges in machine learning research. From random at the start of training, the weights of a neural network evolve in such a way as to be able to perform a variety of tasks, like classifying images. Here we study the emergence of structure in the weights by applying methods from topological data analysis. We train simple feedforward neural networks on the MNIST dataset and monitor the evolution of the weights. When initialized to zero, the weights follow trajectories that branch off recurrently, thus generating trees that describe the growth of the effective capacity of each layer. When initialized to tiny random values, the weights evolve smoothly along two-dimensional surfaces. We show that natural coordinates on these learning surfaces correspond to important factors of variation.