{"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/random-hinge-forest-for-differentiable","title":"Random Hinge Forest for Differentiable Learning","arxiv_id":"1802.03882","date":"2018-02-12","proceeding":null,"authors":["Nathan Lay","Adam P. Harrison","Sharon Schreiber","Gitesh Dawer","Adrian Barbu"],"abstract":"We propose random hinge forests, a simple, efficient, and novel variant of\ndecision forests. Importantly, random hinge forests can be readily incorporated\nas a general component within arbitrary computation graphs that are optimized\nend-to-end with stochastic gradient descent or variants thereof. We derive\nrandom hinge forest and ferns, focusing on their sparse and efficient nature,\ntheir min-max margin property, strategies to initialize them for arbitrary\nnetwork architectures, and the class of optimizers most suitable for optimizing\nrandom hinge forest. The performance and versatility of random hinge forests\nare demonstrated by experiments incorporating a variety of of small and large\nUCI machine learning data sets and also ones involving the MNIST, Letter, and\nUSPS image datasets. We compare random hinge forests with random forests and\nthe more recent backpropagating deep neural decision forests.","url_abs":"http://arxiv.org/abs/1802.03882v2","url_pdf":"http://arxiv.org/pdf/1802.03882v2.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":"random-hinge-forest-for-differentiable","repo_url":"https://github.com/nslay/bleak","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.03882","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}