{"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/thermostat-assisted-continuously-tempered","title":"Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning","arxiv_id":"1711.11511","date":"2017-11-30","proceeding":"NeurIPS 2018 12","authors":["Rui Luo","Jianhong Wang","Yaodong Yang","Zhanxing Zhu","Jun Wang"],"abstract":"We propose a new sampling method, the thermostat-assisted\ncontinuously-tempered Hamiltonian Monte Carlo, for Bayesian learning on large\ndatasets and multimodal distributions. It simulates the Nos\\'e-Hoover dynamics\nof a continuously-tempered Hamiltonian system built on the distribution of\ninterest. A significant advantage of this method is that it is not only able to\nefficiently draw representative i.i.d. samples when the distribution contains\nmultiple isolated modes, but capable of adaptively neutralising the noise\narising from mini-batches and maintaining accurate sampling. While the\nproperties of this method have been studied using synthetic distributions,\nexperiments on three real datasets also demonstrated the gain of performance\nover several strong baselines with various types of neural networks plunged in.","url_abs":"http://arxiv.org/abs/1711.11511v5","url_pdf":"http://arxiv.org/pdf/1711.11511v5.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":"thermostat-assisted-continuously-tempered","repo_url":"https://github.com/hsvgbkhgbv/TACTHMC","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.11511","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}