# Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent

## TL;DR

This paper demonstrates that wide neural networks behave like linear models during training, with their dynamics governed by a first-order Taylor expansion, and their predictions linked to Gaussian processes, providing a simplified theoretical understanding.

## Contribution

The work shows that in the infinite width limit, neural networks' training dynamics are linear and can be accurately approximated by a first-order Taylor expansion, bridging neural networks and Gaussian processes.

## Key findings

- Linearized models accurately predict training dynamics.
- Wide neural networks' predictions match Gaussian process priors.
- Empirical results show strong agreement even for finite networks.

## Abstract

A longstanding goal in deep learning research has been to precisely characterize training and generalization. However, the often complex loss landscapes of neural networks have made a theory of learning dynamics elusive. In this work, we show that for wide neural networks the learning dynamics simplify considerably and that, in the infinite width limit, they are governed by a linear model obtained from the first-order Taylor expansion of the network around its initial parameters. Furthermore, mirroring the correspondence between wide Bayesian neural networks and Gaussian processes, gradient-based training of wide neural networks with a squared loss produces test set predictions drawn from a Gaussian process with a particular compositional kernel. While these theoretical results are only exact in the infinite width limit, we nevertheless find excellent empirical agreement between the predictions of the original network and those of the linearized version even for finite practically-sized networks. This agreement is robust across different architectures, optimization methods, and loss functions.