{"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/logitboost-autoregressive-networks","title":"LogitBoost autoregressive networks","arxiv_id":"1703.07506","date":"2017-03-22","proceeding":null,"authors":["Marc Goessling"],"abstract":"Multivariate binary distributions can be decomposed into products of\nunivariate conditional distributions. Recently popular approaches have modeled\nthese conditionals through neural networks with sophisticated weight-sharing\nstructures. It is shown that state-of-the-art performance on several standard\nbenchmark datasets can actually be achieved by training separate probability\nestimators for each dimension. In that case, model training can be trivially\nparallelized over data dimensions. On the other hand, complexity control has to\nbe performed for each learned conditional distribution. Three possible methods\nare considered and experimentally compared. The estimator that is employed for\neach conditional is LogitBoost. Similarities and differences between the\nproposed approach and autoregressive models based on neural networks are\ndiscussed in detail.","url_abs":"http://arxiv.org/abs/1703.07506v1","url_pdf":"http://arxiv.org/pdf/1703.07506v1.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":"logitboost-autoregressive-networks","repo_url":"https://github.com/goessling/lbarn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}