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• W3034795257 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." @default.
• W3034795257 created "2020-06-19" @default.
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• W3034795257 date "2020-06-10" @default.
• W3034795257 modified "2023-09-27" @default.
• W3034795257 title "Gaussian Gated Linear Networks" @default.
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• W3034795257 hasPublicationYear "2020" @default.
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