Half-Layered Neural Networks

Ethem Alpaydin

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arXiv · 2506.04352

Half-Layered Neural Networks

Ethem Alpaydin

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Abstract

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.

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Ethem Alpaydin. 2025-06-04. Half-Layered Neural Networks. https://arxiv.org/abs/2506.04352

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