{"id":27329443,"url":"https://github.com/csirmaz/trained-linearization","last_synced_at":"2025-08-24T15:09:00.594Z","repository":{"id":72744505,"uuid":"196472223","full_name":"csirmaz/trained-linearization","owner":"csirmaz","description":"Interpreting neural networks by reducing nonlinearities during training","archived":false,"fork":false,"pushed_at":"2019-07-22T22:58:10.000Z","size":114,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-04-12T12:43:40.040Z","etag":null,"topics":["interpretability","linearization","lua","machine-learning","neural-network","rule-extraction","torch"],"latest_commit_sha":null,"homepage":"","language":"TeX","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/csirmaz.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2019-07-11T22:23:56.000Z","updated_at":"2024-09-29T03:36:55.000Z","dependencies_parsed_at":null,"dependency_job_id":"ffe39509-2c4f-4da2-9973-6dcd0c1ab41f","html_url":"https://github.com/csirmaz/trained-linearization","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/csirmaz/trained-linearization","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/csirmaz%2Ftrained-linearization","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/csirmaz%2Ftrained-linearization/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/csirmaz%2Ftrained-linearization/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/csirmaz%2Ftrained-linearization/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/csirmaz","download_url":"https://codeload.github.com/csirmaz/trained-linearization/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/csirmaz%2Ftrained-linearization/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":271895131,"owners_count":24840090,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-08-24T02:00:11.135Z","response_time":111,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["interpretability","linearization","lua","machine-learning","neural-network","rule-extraction","torch"],"created_at":"2025-04-12T12:33:17.678Z","updated_at":"2025-08-24T15:09:00.586Z","avatar_url":"https://github.com/csirmaz.png","language":"TeX","readme":"\n# Interpreting Neural Networks by Reducing Nonlinearities during Training\n\nThis repo contains a short paper and sample code demonstrating\na simple solution that makes it possible to\nextract rules from a neural network that employs Parametric Rectified Linear Units (PReLUs).\nWe introduce a force, applied in parallel to backpropagation, that\naims to reduce PReLUs into the identity function, which then causes\nthe neural network to collapse into a smaller system of linear functions and inequalities\nsuitable for review or use by human decision makers.\n\nAs this force reduces the capacity of neural networks, it is expected to help avoid overfitting as well.\n\nDownload the article in PDF format from the latest release at https://github.com/csirmaz/trained-linearization/releases/latest .\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcsirmaz%2Ftrained-linearization","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcsirmaz%2Ftrained-linearization","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcsirmaz%2Ftrained-linearization/lists"}