2 layer neural networks as Wasserstein gradient flows

Artificial neural networks (ANNs) consist of layers of artificial "neurons" which take in information from the previous layer and output information to neurons in the next layer. Gradient descent is a common method for updating the weights of each neuron based on training data. While in practice every layer of a neural network has only finitely many neurons, it is beneficial to consider a neural network layer with infinitely many neurons, for the sake of developing a theory that explains how ANNs work. In particular, from this viewpoint the process of updating the neuron weights for a shallow neural network can be described by a Wasserstein gradient flow.

Shallow Neural Networks

Let us introduce the mathematical framework and notation for a neural network with a single hidden layer. Let $D\subset \mathbb {R} ^{d}$ be open . The set $D$ represents the space of inputs into the network. There is some unknown function $f:D\rightarrow \mathbb {R}$ which we would like to approximate. Let $N\in \mathbb {N}$ be the number of neurons in the hidden layer. Define

$F_{N}:D\times W\times U\rightarrow \mathbb {R} ^{k}$

be given by

$F_{N}(x,\omega _{1},\dots ,\omega _{N},\theta _{1},\dots ,\theta _{N})={\frac {1}{N}}\sum _{i=1}^{N}\omega _{i}h(\theta _{i},x)$

where $h$ is a fixed activation function. The goal is to use training data to repeatedly update the weights $\omega _{i}$ and the $\theta _{i}$ based on how close $f_{N,\omega ,\theta }:=F_{N}(\cdot ,\omega _{1},\dots ,\omega _{N},\theta _{1},\dots ,\theta _{N})$ is to the function $f$. More concretely, we want to find $\omega ,\theta$ that minimizes the loss function:

$l(f,f_{N,\omega ,\theta }):={\frac {1}{2}}\int _{D}|f(x)-f_{N,\omega ,\theta }(x)|^{2}dx$

A standard way to choose an update the weights is to start with a random choice of weights ${\bar {\omega }}$