{"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/fixing-implicit-derivatives-trust-region","title":"Fixing Implicit Derivatives: Trust-Region Based Learning of Continuous Energy Functions","arxiv_id":null,"date":"2019-12-01","proceeding":"NeurIPS 2019 12","authors":["Chris Russell","Matteo Toso","Neill Campbell"],"abstract":"We present a new technique for the learning of continuous energy functions that\nwe refer to as Wibergian Learning. One common approach to inverse problems\nis to cast them as an energy minimisation problem, where the minimum cost\nsolution found is used as an estimator of hidden parameters. Our new approach\nformally characterises the dependency between weights that control the shape of\nthe energy function, and the location of minima, by describing minima as fixed\npoints of optimisation methods. This allows for the use of gradient-based end-to-\nend training to integrate deep-learning and the classical inverse problem methods.\nWe show how our approach can be applied to obtain state-of-the-art results in the\ndiverse applications of tracker fusion and multiview 3D reconstruction.","url_abs":"http://papers.nips.cc/paper/8427-fixing-implicit-derivatives-trust-region-based-learning-of-continuous-energy-functions","url_pdf":"http://papers.nips.cc/paper/8427-fixing-implicit-derivatives-trust-region-based-learning-of-continuous-energy-functions.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":"fixing-implicit-derivatives-trust-region","repo_url":"https://github.com/MatteoT90/WibergianLearning","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"3d-reconstruction","task_name":"3D Reconstruction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}