# Half-Layered Neural Networks

arXiv: https://arxiv.org/abs/2506.04352 · HTML: https://mlsanity.com/paper/2506.04352/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 5 references that are in the map.
- First posted on arXiv 4 Jun 2025.

## Builds on (most cited first, 1 of 1)

- [Deep Randomized Neural Networks](https://mlsanity.com/paper/2002.12287/) (2020)

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

- [HalfNet: Randomized Neural Networks with Learned Subspace Geometry](https://mlsanity.com/paper/2606.04583/) (2026)

## Abstract (arXiv)

We propose a ``half'' layer of hidden units that has some of its weights randomly set and some of them trained. A half unit is composed of two stages: First, it takes a weighted sum of its inputs with fixed random weights, and second, the total activation is multiplied and then translated using two modifiable weights, before the result is passed through a nonlinearity. The number of modifiable weights of each hidden unit is thus two and does not depend on the fan-in. We show how such half units can be used in the first or any later layer in a deep network, possibly following convolutional layers. Our experiments on MNIST and FashionMNIST data sets indicate the promise of half layers, where we can achieve reasonable accuracy with a reduced number of parameters due to the regularizing effect of the randomized connections.