# Nonparametric Weight Initialization of Neural Networks via Integral Representation

arXiv: https://arxiv.org/abs/1312.6461 · HTML: https://mlsanity.com/paper/1312.6461/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2013.

## Built on by (most cited first, 1 of 1)

- [Nonuniform random feature models using derivative information](https://mlsanity.com/paper/2410.02132/) (2024)

## Abstract (arXiv)

A new initialization method for hidden parameters in a neural network is proposed. Derived from the integral representation of the neural network, a nonparametric probability distribution of hidden parameters is introduced. In this proposal, hidden parameters are initialized by samples drawn from this distribution, and output parameters are fitted by ordinary linear regression. Numerical experiments show that backpropagation with proposed initialization converges faster than uniformly random initialization. Also it is shown that the proposed method achieves enough accuracy by itself without backpropagation in some cases.