{"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/bayesian-optimization-for-parameter-tuning-of","title":"Bayesian Optimization for Parameter Tuning of the XOR Neural Network","arxiv_id":"1709.07842","date":"2017-09-22","proceeding":null,"authors":["Lawrence Stewart","Mark Stalzer"],"abstract":"When applying Machine Learning techniques to problems, one must select model\nparameters to ensure that the system converges but also does not become stuck\nat the objective function's local minimum. Tuning these parameters becomes a\nnon-trivial task for large models and it is not always apparent if the user has\nfound the optimal parameters. We aim to automate the process of tuning a Neural\nNetwork, (where only a limited number of parameter search attempts are\navailable) by implementing Bayesian Optimization. In particular, by assigning\nGaussian Process Priors to the parameter space, we utilize Bayesian\nOptimization to tune an Artificial Neural Network used to learn the XOR\nfunction, with the result of achieving higher prediction accuracy.","url_abs":"http://arxiv.org/abs/1709.07842v2","url_pdf":"http://arxiv.org/pdf/1709.07842v2.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":"bayesian-optimization-for-parameter-tuning-of","repo_url":"https://github.com/LawrenceMMStewart/Bayesian_Optimization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}