# Gaussian Gated Linear Networks

arXiv: https://arxiv.org/abs/2006.05964 · HTML: https://mlsanity.com/paper/2006.05964/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2020.

## Built on by (most cited first, 6 of 6)

- [The Neural Race Reduction: Dynamics of Abstraction in Gated Networks](https://mlsanity.com/paper/2207.10430/) (2022)
- [The Influence of Learning Rule on Representation Dynamics in Wide Neural Networks](https://mlsanity.com/paper/2210.02157/) (2022)
- [Globally Gated Deep Linear Networks](https://mlsanity.com/paper/2210.17449/) (2022)
- [Levin Tree Search with Context Models](https://mlsanity.com/paper/2305.16945/) (2023)
- [Gated Linear Model induced U-net for surrogate modeling and uncertainty quantification](https://mlsanity.com/paper/2111.05123/) (2021)
- [Hierarchical Partitioning Forecaster](https://mlsanity.com/paper/2305.13063/) (2023)

## Abstract (arXiv)

We propose the Gaussian Gated Linear Network (G-GLN), an extension to the recently proposed GLN family of deep neural networks. Instead of using backpropagation to learn features, GLNs have a distributed and local credit assignment mechanism based on optimizing a convex objective. This gives rise to many desirable properties including universality, data-efficient online learning, trivial interpretability and robustness to catastrophic forgetting. We extend the GLN framework from classification to multiple regression and density modelling by generalizing geometric mixing to a product of Gaussian densities. The G-GLN achieves competitive or state-of-the-art performance on several univariate and multivariate regression benchmarks, and we demonstrate its applicability to practical tasks including online contextual bandits and density estimation via denoising.