{"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/disjunctive-normal-networks","title":"Disjunctive Normal Networks","arxiv_id":"1412.8534","date":"2014-12-30","proceeding":null,"authors":["Mehdi Sajjadi","Mojtaba Seyedhosseini","Tolga Tasdizen"],"abstract":"Artificial neural networks are powerful pattern classifiers; however, they\nhave been surpassed in accuracy by methods such as support vector machines and\nrandom forests that are also easier to use and faster to train.\nBackpropagation, which is used to train artificial neural networks, suffers\nfrom the herd effect problem which leads to long training times and limit\nclassification accuracy. We use the disjunctive normal form and approximate the\nboolean conjunction operations with products to construct a novel network\narchitecture. The proposed model can be trained by minimizing an error function\nand it allows an effective and intuitive initialization which solves the\nherd-effect problem associated with backpropagation. This leads to state-of-the\nart classification accuracy and fast training times. In addition, our model can\nbe jointly optimized with convolutional features in an unified structure\nleading to state-of-the-art results on computer vision problems with fast\nconvergence rates. A GPU implementation of LDNN with optional convolutional\nfeatures is also available","url_abs":"http://arxiv.org/abs/1412.8534v1","url_pdf":"http://arxiv.org/pdf/1412.8534v1.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":"disjunctive-normal-networks","repo_url":"https://github.com/tsitsimis/neural-disjunctive-normal-networks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":null,"task_name":"GPU"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}