{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/gaussian-gated-linear-networks","title":"Gaussian Gated Linear Networks","arxiv_id":"2006.05964","date":"2020-06-10","proceeding":"NeurIPS 2020 12","authors":["David Budden","Adam Marblestone","Eren Sezener","Tor Lattimore","Greg Wayne","Joel Veness"],"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.","url_abs":"https://arxiv.org/abs/2006.05964v2","url_pdf":"https://arxiv.org/pdf/2006.05964v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"gaussian-gated-linear-networks","repo_url":"https://github.com/deepmind/deepmind-research/tree/master/gated_linear_networks","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"gaussian-gated-linear-networks","repo_url":"https://github.com/2023-MindSpore-4/Code12/tree/main/d2l/chapter_03_linear-networks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"gaussian-gated-linear-networks","repo_url":"https://github.com/MindCode-4/code-7/tree/main/gaussian_adaptive_attention","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"density-estimation","task_name":"Density Estimation"},{"task_slug":"multi-armed-bandits","task_name":"Multi-Armed Bandits"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"g-gln","method_name":"G-GLN"},{"method_slug":"g-gln-neuron","method_name":"G-GLN Neuron"},{"method_slug":"gln","method_name":"GLN"},{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[{"slug":"g-gln","name":"G-GLN","full_name":"Gaussian Gated Linear Network"},{"slug":"g-gln-neuron","name":"G-GLN Neuron","full_name":"G-GLN Neuron"}],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2006.05964","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}