# A Gaussian Process perspective on Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1810.10798 · HTML: https://mlsanity.com/paper/1810.10798/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2018.

## Built on by (most cited first, 4 of 4)

- [Fast Finite Width Neural Tangent Kernel](https://mlsanity.com/paper/2206.08720/) (2022)
- [Quantitative Gaussian Approximation of Randomly Initialized Deep Neural Networks](https://mlsanity.com/paper/2203.07379/) (2022)
- [Learning from Randomly Initialized Neural Network Features](https://mlsanity.com/paper/2202.06438/) (2022)
- [Neural Hilbert Ladders: Multi-Layer Neural Networks in Function Space](https://mlsanity.com/paper/2307.01177/) (2023)

## Abstract (arXiv)

In this paper we cast the well-known convolutional neural network in a Gaussian process perspective. In this way we hope to gain additional insights into the performance of convolutional networks, in particular understand under what circumstances they tend to perform well and what assumptions are implicitly made in the network. While for fully-connected networks the properties of convergence to Gaussian processes have been studied extensively, little is known about situations in which the output from a convolutional network approaches a multivariate normal distribution.