Gaussian Gated Linear Networks

• URL: http://arxiv.org/abs/2006.05964v2
• Date: Wed, 21 Oct 2020 16:39:03 GMT
• Title: Gaussian Gated Linear Networks
• Authors: David Budden, Adam Marblestone, Eren Sezener, Tor Lattimore, Greg Wayne, Joel Veness
• Abstract summary: 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.
• Score: 32.27304928359326
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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