Title: Convergence Results for Neural Networks via Electrodynamics,
Abstract: We study whether a depth two neural network can learn another depth two
network using gradient descent. Assuming a linear output node, we show that the
question of whether gradient descent converges to the target function is
equivalent to the following question in electrodynamics: Given $k$ fixed
protons in $\mathbb{R}^d,$ and $k$ electrons, each moving due to the attractive
force from the protons and repulsive force from the remaining electrons,
whether at equilibrium all the electrons will be matched up with the protons,
up to a permutation. Under the standard electrical force, this follows from the
classic Earnshaw's theorem. In our setting, the force is determined by the
activation function and the input distribution. Building on this equivalence,
we prove the existence of an activation function such that gradient descent
learns at least one of the hidden nodes in the target network. Iterating, we
show that gradient descent can be used to learn the entire network one node at
a time.