{"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/ginn-geometric-illustration-of-neural","title":"GINN: Geometric Illustration of Neural Networks","arxiv_id":"1810.01860","date":"2018-10-02","proceeding":null,"authors":["Luke N. Darlow","Amos J. Storkey"],"abstract":"This informal technical report details the geometric illustration of decision\nboundaries for ReLU units in a three layer fully connected neural network. The\nnetwork is designed and trained to predict pixel intensity from an (x, y) input\nlocation. The Geometric Illustration of Neural Networks (GINN) tool was built\nto visualise and track the points at which ReLU units switch from being active\nto off (or vice versa) as the network undergoes training. Several phenomenon\nwere observed and are discussed herein. This technical report is a supporting\ndocument to the blog post with online demos and is available at\nhttp://www.bayeswatch.com/2018/09/17/GINN/.","url_abs":"http://arxiv.org/abs/1810.01860v1","url_pdf":"http://arxiv.org/pdf/1810.01860v1.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":"ginn-geometric-illustration-of-neural","repo_url":"https://github.com/learning-luke/ginn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[{"method_slug":"relu","method_name":"ReLU"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}