{"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/boltzmann-machines-and-energy-based-models","title":"Boltzmann machines and energy-based models","arxiv_id":"1708.06008","date":"2017-08-20","proceeding":null,"authors":["Takayuki Osogami"],"abstract":"We review Boltzmann machines and energy-based models. A Boltzmann machine\ndefines a probability distribution over binary-valued patterns. One can learn\nparameters of a Boltzmann machine via gradient based approaches in a way that\nlog likelihood of data is increased. The gradient and Hessian of a Boltzmann\nmachine admit beautiful mathematical representations, although computing them\nis in general intractable. This intractability motivates approximate methods,\nincluding Gibbs sampler and contrastive divergence, and tractable alternatives,\nnamely energy-based models.","url_abs":"http://arxiv.org/abs/1708.06008v2","url_pdf":"http://arxiv.org/pdf/1708.06008v2.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":"boltzmann-machines-and-energy-based-models","repo_url":"https://github.com/Kevin-Sean-Chen/Restriced_Boltzmann_Machine","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1708.06008","atlas_url":"https://app.syntology.ai/?focus=1708.06008","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}